Adaptive trust profile endpoint

ABSTRACT

A system, method, and computer-readable medium are disclosed for generating an adaptive trust profile via an adaptive trust profile operation. In various embodiments the adaptive trust profile operation includes: monitoring a plurality of electronically-observable actions of an entity, the plurality of electronically-observable actions of the entity corresponding to a respective plurality of events enacted by the entity, the monitoring comprising monitoring at least one of the plurality of electronically-observable actions via a protected endpoint; converting the plurality of electronically-observable actions of the entity to electronic information representing the plurality of actions of the entity; and generating an adaptive trust profile based upon the action of the entity.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention relates in general to the field of computers andsimilar technologies, and in particular to software utilized in thisfield. Still more particularly, it relates to a method, system andcomputer-usable medium for using entity profile attributes to adaptivelymitigate risk.

Description of the Related Art

Users interact with physical, system, data, and services resources ofall kinds, as well as each other, on a daily basis. Each of theseinteractions, whether accidental or intended, poses some degree ofsecurity risk, depending on the behavior of the user. In particular, theactions of a formerly trusted user may become malicious as a result ofbeing subverted, compromised or radicalized due to any number ofinternal or external factors or stressors. For example, financialpressure, political idealism, irrational thoughts, or other influencesmay adversely affect a user's intent and/or behavior.

SUMMARY OF THE INVENTION

In one embodiment the invention relates to a method for generating anadaptive trust profile, comprising: monitoring a plurality ofelectronically-observable actions of an entity, the plurality ofelectronically-observable actions of the entity corresponding to arespective plurality of events enacted by the entity, the monitoringcomprising monitoring at least one of the plurality ofelectronically-observable actions via a protected endpoint; convertingthe plurality of electronically-observable actions of the entity toelectronic information representing the plurality of actions of theentity; and generating an adaptive trust profile based upon the actionof the entity.

In another embodiment the invention relates to a system comprising: aprocessor; a data bus coupled to the processor; and a non-transitory,computer-readable storage medium embodying computer program code, thenon-transitory, computer-readable storage medium being coupled to thedata bus, the computer program code interacting with a plurality ofcomputer operations and comprising instructions executable by theprocessor and configured for: monitoring a plurality ofelectronically-observable actions of an entity, the plurality ofelectronically-observable actions of the entity corresponding to arespective plurality of events enacted by the entity, the monitoringcomprising monitoring at least one of the plurality ofelectronically-observable actions via a protected endpoint; convertingthe plurality of electronically-observable actions of the entity toelectronic information representing the plurality of actions of theentity; and generating an adaptive trust profile based upon the actionof the entity.

In another embodiment the invention relates to a computer-readablestorage medium embodying computer program code, the computer programcode comprising computer executable instructions configured for:monitoring a plurality of electronically-observable actions of anentity, the plurality of electronically-observable actions of the entitycorresponding to a respective plurality of events enacted by the entity,the monitoring comprising monitoring at least one of the plurality ofelectronically-observable actions via a protected endpoint; convertingthe plurality of electronically-observable actions of the entity toelectronic information representing the plurality of actions of theentity; and generating an adaptive trust profile based upon the actionof the entity.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention may be better understood, and its numerousobjects, features and advantages made apparent to those skilled in theart by referencing the accompanying drawings. The use of the samereference number throughout the several figures designates a like orsimilar element.

FIG. 1 depicts an exemplary client computer in which the presentinvention may be implemented;

FIG. 2 is a simplified block diagram of an edge device;

FIG. 3 is a simplified block diagram of an endpoint agent;

FIG. 4 is a simplified block diagram of a security analytics system;

FIG. 5 is a simplified block diagram of a security analytics system;

FIG. 6 is a simplified block diagram of an adaptive trust profile (ATP);

FIG. 7 is simplified block diagram of process flows associated with theoperation of an ATP system;

FIGS. 8a and 8b shows a block diagram of a security analytics systemenvironment;

FIG. 9 shows a functional block diagram of the operation of an ATPsystem;

FIG. 10 is a table showing components of an ATP;

FIG. 11 is a table showing analytic utility actions occurring during asession;

FIG. 12 is a simplified block diagram of an ATP system environment;

FIG. 13 is a generalized flowchart of the performance of session-basedfingerprint generation operations;

FIGS. 14a and 14b are a generalized flowchart of the performance of ATPdefinition and management operations;

FIG. 15 is a simplified block diagram of a security analytics systemimplemented to adaptively assess risk associated with an entitybehavior;

FIG. 16 is a simplified block diagram of the operation of a securityanalytics system to adaptively respond to an entity request; and

FIG. 17 is a generalized flowchart of the performance of securityanalytics system operations for adaptively managing entity behaviorrisk.

DETAILED DESCRIPTION

A method, system and computer-usable medium are disclosed for usingentity profile attributes to adaptively mitigate risk. Certain aspectsof the invention include an appreciation that the existence of anyentity, whether it is an individual user, a group of users, anorganization, a device, a system, a network, an account, a domain, anoperation, a process, a software application, or a service, representssome degree of security risk. Various aspects of the invention likewiseinclude an appreciation that certain non-user entities, such ascomputing, communication, and surveillance devices can be a source fortelemetry associated with certain events and entity behaviors. Likewise,various aspects of the invention include an appreciation that certainaccounts may be global, spanning multiple devices, such as adomain-level account allowing an entity access to multiple systems.Certain aspects of the invention likewise include an appreciation that aparticular account may be shared by multiple entities.

Accordingly, certain aspects of the invention include an appreciationthat a particular entity can be assigned a measure of risk according toits respective attributes, associated behavioral models, and resultantinferences contained in an associated profile. As an example, a firstprofile may have an attribute that its corresponding entity works in thehuman resource department, while a second profile may have an attributethat its corresponding entity is an email server. To continue theexample, the first profile may have an associated behavioral model thatindicates its corresponding entity is not acting as they did the daybefore, while the second profile may have an associated behavioral modelthat indicates its corresponding entity is connecting to a suspicious IPaddress. To further continue the example, the first profile may have aresultant inference that its corresponding entity is likely to beleaving the company, while the second profile may have a resultantinference that there is a high probability its corresponding entity iscompromised. Certain embodiments of the invention likewise include anappreciation that the measure of risk assigned to a particular entitycan be adaptively revised according to corresponding changes in itsrespective attributes, associated behavioral models, and resultantinferences contained in an associated profile.

For the purposes of this disclosure, an information handling system mayinclude any instrumentality or aggregate of instrumentalities operableto compute, classify, process, transmit, receive, retrieve, originate,switch, store, display, manifest, detect, record, reproduce, handle, orutilize any form of information, intelligence, or data for business,scientific, control, entertainment, or other purposes. For example, aninformation handling system may be a personal computer, a mobile devicesuch as a tablet or smartphone, a consumer electronic device, aconnected “smart device,” a network appliance, a network storage device,a network gateway device, a server or collection of servers or any othersuitable device and may vary in size, shape, performance, functionality,and price. The information handling system may include volatile and/ornon-volatile memory, and one or more processing resources such as acentral processing unit (CPU) or hardware or software control logic.Additional components of the information handling system may include oneor more storage systems, one or more wired or wireless interfaces forcommunicating with other networked devices, external devices, andvarious input and output (I/O) devices, such as a keyboard, a mouse, amicrophone, speakers, a track pad, a touchscreen and a display device(including a touch sensitive display device). The information handlingsystem may also include one or more buses operable to transmitcommunication between the various hardware components.

For the purposes of this disclosure, computer-readable media may includeany instrumentality or aggregation of instrumentalities that may retaindata and/or instructions for a period of time. Computer-readable mediamay include, without limitation, storage media such as a direct accessstorage device (e.g., a hard disk drive or solid state drive), asequential access storage device (e.g., a tape disk drive), opticalstorage device, random access memory (RAM), read-only memory (ROM),electrically erasable programmable read-only memory (EEPROM), and/orflash memory; as well as communications media such as wires, opticalfibers, microwaves, radio waves, and other electromagnetic and/oroptical carriers; and/or any combination of the foregoing.

FIG. 1 is a generalized illustration of an information handling system100 that can be used to implement the system and method of the presentinvention. The information handling system 100 includes a processor(e.g., central processor unit or “CPU”) 102, input/output (I/O) devices104, such as a display, a keyboard, a mouse, and associated controllers,a storage system 106, and various other subsystems 108. In variousembodiments, the information handling system 100 also includes networkport 110 operable to connect to a network 140, which is likewiseaccessible by a service provider server 142. The information handlingsystem 100 likewise includes system memory 112, which is interconnectedto the foregoing via one or more buses 114. System memory 112 furtherincludes operating system (OS) 116 and in various embodiments may alsoinclude a security analytics system 118. In one embodiment, theinformation handling system 100 is able to download the securityanalytics system 118 from the service provider server 142. In anotherembodiment, the security analytics system 118 is provided as a servicefrom the service provider server 142.

In various embodiments, the security analytics system 118 performs asecurity analytics operation. In certain embodiments, the securityanalytics operation improves processor efficiency, and thus theefficiency of the information handling system 100, by facilitatingsecurity analytics functions. As will be appreciated, once theinformation handling system 100 is configured to perform the securityanalytics operation, the information handling system 100 becomes aspecialized computing device specifically configured to perform thesecurity analytics operation and is not a general purpose computingdevice. Moreover, the implementation of the security analytics system118 on the information handling system 100 improves the functionality ofthe information handling system 100 and provides a useful and concreteresult of performing security analytics functions to mitigate securityrisk. In certain embodiments, the security analytics system 118 may beimplemented to include an adaptable trust profile (ATP) system 120. Incertain embodiments, the ATP system 120 may be implemented to performvarious ATP operations, described in greater detail herein.

FIG. 2 is a simplified block diagram of an edge device implemented inaccordance with an embodiment of the invention. As used herein, an edgedevice, such as the edge device 202 shown in FIG. 2, broadly refers to adevice providing an entry point into a network 140. Examples of suchedge devices 202 may include routers, routing switches, integratedaccess devices (IADs), multiplexers, wide-area network (WAN) accessdevices, and network security appliances. In certain embodiments, thenetwork 140 may be a private network (e.g., an enterprise network), asemi-public network (e.g., a service provider core network), or a publicnetwork (e.g., the Internet).

Skilled practitioners of the art will be aware that edge devices 202 areoften implemented as routers that provide authenticated access tofaster, more efficient backbone and core networks. Furthermore, currentindustry trends include making edge devices 202 more intelligent, whichallows core devices to operate at higher speed as they are not burdenedwith additional administrative overhead. Accordingly, such edge devices202 often include Quality of Service (QoS) and multi-service functionsto manage different types of traffic. Consequently, it is common todesign core networks with switches that use routing protocols such asOpen Shortest Path First (OSPF) or Multiprotocol Label Switching (MPLS)for reliability and scalability. Such approaches allow edge devices 202to have redundant links to the core network, which not only providesimproved reliability, but enables enhanced, flexible, and scalablesecurity capabilities as well.

In certain embodiments, the edge device 202 may be implemented toinclude a communications/services architecture 204, various pluggablecapabilities 212, a traffic router 210, and a pluggable hostingframework 208. In certain embodiments, the communications/servicesarchitecture 202 may be implemented to provide access to and fromvarious networks 140, cloud services 206, or a combination thereof. Incertain embodiments, the cloud services 206 may be provided by a cloudinfrastructure familiar to those of skill in the art. In certainembodiments, the edge device 202 may be implemented to provide supportfor a variety of generic services, such as directory integration,logging interfaces, update services, and bidirectional risk/contextflows associated with various analytics. In certain embodiments, theedge device 202 may be implemented to provide temporal information,described in greater detail herein, associated with the provision ofsuch services.

In certain embodiments, the edge device 202 may be implemented as ageneric device configured to host various network communications, dataprocessing, and security management capabilities. In certainembodiments, the pluggable hosting framework 208 may be implemented tohost such capabilities in the form of pluggable capabilities 212. Incertain embodiments, the pluggable capabilities 212 may includecapability ‘1’ 214 (e.g., basic firewall), capability ‘2’ 216 (e.g.,general web protection), capability ‘3’ 218 (e.g., data sanitization),and so forth through capability ‘n’ 220, which may include capabilitiesneeded for a particular operation, process, or requirement on anas-needed basis. In certain embodiments, such capabilities may includethe performance of operations associated with managing an adaptive trustProfile (ATP), described in greater detail herein. In certainembodiments, such operations may include the provision of associatedtemporal information (e.g., time stamps).

In certain embodiments, the pluggable capabilities 212 may be sourcedfrom various cloud services 206. In certain embodiments, the pluggablehosting framework 208 may be implemented to provide certain computingand communication infrastructure components, and foundationcapabilities, required by one or more of the pluggable capabilities 212.In certain embodiments, the pluggable hosting framework 208 may beimplemented to allow the pluggable capabilities 212 to be dynamicallyinvoked. Skilled practitioners of the art will recognize that many suchembodiments are possible. Accordingly, the foregoing is not intended tolimit the spirit, scope or intent of the invention.

FIG. 3 is a simplified block diagram of an endpoint agent implemented inaccordance with an embodiment of the invention. As used herein, anendpoint agent 306 broadly refers to a software agent used incombination with an endpoint device 304 to establish a protectedendpoint 302. Skilled practitioners of the art will be familiar withsoftware agents, which are computer programs that perform actions onbehalf of a user or another program. In various approaches, a softwareagent may be autonomous or work together with another agent or a user.In certain of these approaches the software agent is implemented toautonomously decide if a particular action is appropriate for a givenevent, such as an observed entity behavior.

An endpoint device 304, as likewise used herein, refers to aninformation processing system such as a personal computer, a laptopcomputer, a tablet computer, a personal digital assistant (PDA), a smartphone, a mobile telephone, a digital camera, a video camera, or otherdevice capable of storing, processing and communicating data. In certainembodiments, the communication of the data may take place in real-timeor near-real-time. As used herein, real-time broadly refers toprocessing and providing information within a time interval brief enoughto not be discernable by a user. As an example, a cellular phoneconversation may be used to communicate information in real-time, whilean instant message (IM) exchange may be used to communicate informationin near real-time. In certain embodiments, the communication of theinformation may take place asynchronously. For example, an email messagemay be stored on an endpoint device 304 when it is offline. In thisexample, the information may be communicated to its intended recipientonce the endpoint device 304 gains access to a network 140.

A protected endpoint 302, as likewise used herein, broadly refers to apolicy-based approach to network security that typically requiresendpoint devices 304 to comply with certain criteria before they aregranted access to network resources. As an example, a given endpointdevice 304 may be required to have a particular operating system (OS),or version thereof, a Virtual Private Network (VPN) client, anti-virussoftware with current updates, and so forth. In certain embodiments, theprotected endpoint 302 may be implemented to perform operationsassociated with providing real-time resolution of the identity of anentity at a particular point in time, as described in greater detailherein. In certain embodiments, the protected endpoint 302 may beimplemented to provide temporal information, such as timestampinformation, associated with such operations.

In certain embodiments, the real-time resolution of the identity of anentity at a particular point in time may be based upon contextualinformation associated with a given entity behavior. As used herein,contextual information broadly refers to any information, directly orindirectly, individually or in combination, related to a particularentity behavior. In certain embodiments, entity behavior may include anentity's physical behavior, cyber behavior, or a combination thereof. Aslikewise used herein, physical behavior broadly refers to any entitybehavior occurring within a physical realm. More particularly, physicalbehavior may include any action enacted by an entity that can beobjectively observed, or indirectly inferred, within a physical realm.

As an example, a user may attempt to use an electronic access card toenter a secured building at a certain time. In this example, the use ofthe access card to enter the building is the action and the reading ofthe access card makes the user's physical behaviorelectronically-observable. As another example, a first user mayphysically transfer a document to a second user, which is captured by avideo surveillance system. In this example, the physical transferal ofthe document from the first user to the second user is the action.Likewise, the video record of the transferal makes the first and seconduser's physical behavior electronically-observable. As used herein,electronically-observable entity behavior broadly refers to any behaviorexhibited or enacted by a entity that can be electronically observed.

Cyber behavior, as used herein, broadly refers to any behavior occurringin cyberspace, whether enacted by an individual user, a group of users,or a system acting at the behest of an individual user, a group ofusers, or an entity. More particularly, cyber behavior may includephysical, social, or mental actions that can be objectively observed, orindirectly inferred, within cyberspace. As an example, a user may use anendpoint device 304 to access and browse a particular website on theInternet. In this example, the individual actions performed by the userto access and browse the website constitute a cyber behavior. As anotherexample, a user may use an endpoint device 304 to download a data filefrom a particular system at a particular point in time. In this example,the individual actions performed by the user to download the data file,and associated temporal information, such as a time-stamp associatedwith the download, constitute a cyber behavior. In these examples, theactions are enacted within cyberspace, in combination with associatedtemporal information, which makes them electronically-observable.

As likewise used herein, cyberspace broadly refers to a network 140environment capable of supporting communication between two or moreentities. In certain embodiments, the entity may be a user, an endpointdevice 304, or various resources, described in greater detail herein. Incertain embodiments, the entities may include various endpoint devices304 or resources operating at the behest of an entity, such as a user.In certain embodiments, the communication between the entities mayinclude audio, image, video, text, or binary data.

As described in greater detail herein, the contextual information mayinclude a user's authentication factors. Contextual information maylikewise include various temporal identity resolution factors, such asidentification factors associated with the entity, thedate/time/frequency of various entity behaviors, the entity's location,the entity's role or position in an organization, their associatedaccess rights, and certain user gestures employed by a user in theenactment of a user behavior. Other contextual information may likewiseinclude various user interactions, whether the interactions are with anendpoint device 304, a network 140, a resource, or another user. Incertain embodiments, entity behaviors, and their related contextualinformation, may be collected at particular points of observation, andat particular points in time, described in greater detail herein. Incertain embodiments, a protected endpoint 302 may be implemented as apoint of observation for the collection of entity behavior andcontextual information.

In certain embodiments, the endpoint agent 306 may be implemented touniversally support a variety of operating systems, such as AppleMacintosh®, Microsoft Windows®, Linux®, Android® and so forth. Incertain embodiments, the endpoint agent 306 may be implemented tointeract with the endpoint device 304 through the use of low-level hooks312 at the operating system level. It will be appreciated that the useof low-level hooks 312 allows the endpoint agent 306 to subscribe tomultiple events through a single hook. Consequently, multiplefunctionalities provided by the endpoint agent 306 can share a singledata stream, using only those portions of the data stream they mayindividually need. Accordingly, system efficiency can be improved andoperational overhead reduced.

In certain embodiments, the endpoint agent 306 may be implemented toprovide a common infrastructure for pluggable feature packs 308. Invarious embodiments, the pluggable feature packs 308 may provide certainsecurity management functionalities. Examples of such functionalitiesmay include various anti-virus and malware detection, data lossprotection (DLP), insider threat detection, and so forth. In certainembodiments, the security management functionalities may include one ormore functionalities associated with providing real-time resolution ofthe identity of an entity at a particular point in time, as described ingreater detail herein.

In certain embodiments, a particular pluggable feature pack 308 isinvoked as needed by the endpoint agent 306 to provide a givenfunctionality. In certain embodiments, individual features of aparticular pluggable feature pack 308 are invoked as needed. It will beappreciated that the ability to invoke individual features of apluggable feature pack 308, without necessarily invoking all suchfeatures, will likely improve the operational efficiency of the endpointagent 306 while simultaneously reducing operational overhead.Accordingly, the endpoint agent 306 can self-optimize in certainembodiments by using the common infrastructure and invoking only thosepluggable components that are applicable or needed for a given userbehavior.

In certain embodiments, the individual features of a pluggable featurepack 308 are invoked by the endpoint agent 306 according to theoccurrence of a particular user behavior. In certain embodiments, theindividual features of a pluggable feature pack 308 are invoked by theendpoint agent 306 according to the occurrence of a particular temporalevent, described in greater detail herein. In certain embodiments, theindividual features of a pluggable feature pack 308 are invoked by theendpoint agent 306 at a particular point in time. In these embodiments,the method by which a given user behavior, temporal event, or point intime is selected is a matter of design choice.

In certain embodiments, the individual features of a pluggable featurepack 308 may be invoked by the endpoint agent 306 according to thecontext of a particular user behavior. As an example, the context may bethe user enacting the user behavior, their associated riskclassification, which resource they may be requesting, the point in timethe user behavior is enacted, and so forth. In certain embodiments, thepluggable feature packs 308 may be sourced from various cloud services206. In certain embodiments, the pluggable feature packs 308 may bedynamically sourced from various cloud services 206 by the endpointagent 306 on an as-need basis.

In certain embodiments, the endpoint agent 306 may be implemented withadditional functionalities, such as event analytics 310. In certainembodiments, the event analytics 310 functionality may include analysisof various user behaviors, described in greater detail herein. Incertain embodiments, the endpoint agent 306 may be implemented with athin hypervisor 314, which can be run at Ring-1, thereby providingprotection for the endpoint agent 306 in the event of a breach. As usedherein, a thin hypervisor broadly refers to a simplified, OS-dependenthypervisor implemented to increase security. As likewise used herein,Ring-1 broadly refers to approaches allowing guest operating systems torun Ring 0 (i.e., kernel) operations without affecting other guests orthe host OS. Those of skill in the art will recognize that many suchembodiments and examples are possible. Accordingly, the foregoing is notintended to limit the spirit, scope or intent of the invention.

FIG. 4 is a simplified block diagram of a security analytics systemimplemented in accordance with an embodiment of the invention. Incertain embodiments, the security analytics system 118 shown in FIG. 4may include an event queue analytics 404 module, described in greaterdetail herein. In certain embodiments, the event queue analytics 404sub-system may be implemented to include an enrichment 406 module and astreaming analytics 408 module. In certain embodiments, the securityanalytics system 118 may be implemented to provide log storage,reporting, and analytics capable of performing streaming 408 andon-demand 410 analytics operations. In certain embodiments, suchoperations may be associated with defining and managing an adaptivetrust profile (ATP), detecting entity behavior that may be of analyticutility, adaptively responding to mitigate risk, or a combinationthereof, as described in greater detail herein. In certain embodiments,entity behavior of analytic utility may be determined to be anomalous,abnormal, unexpected, malicious, or some combination thereof, asdescribed in greater detail herein.

In certain embodiments, the security analytics system 118 may beimplemented to provide a uniform platform for storing events andcontextual information associated with various entity behaviors andperforming longitudinal analytics. As used herein, longitudinalanalytics broadly refers to performing analytics of entity behaviorsoccurring over a particular period of time. As an example, an entity mayiteratively attempt to access certain proprietary information stored invarious locations. In addition, the attempts may occur over a briefperiod of time. To continue the example, the fact that the informationthe entity is attempting to access is proprietary, that it is stored invarious locations, and the attempts are occurring in a brief period oftime, in combination, may indicate the entity behavior enacted by theentity is suspicious. As another example, certain entity identifierinformation (e.g., a user name) associated with an entity may changeover time. In this example, a change in the entity's user name, during aparticular period of time or at a particular point in time, mayrepresent suspicious entity behavior.

In certain embodiments, the security analytics system 118 may beimplemented to be scalable. In certain embodiments, the securityanalytics system 118 may be implemented in a centralized location, suchas a corporate data center. In these embodiments, additional resourcesmay be added to the security analytics system 118 as needs grow. Incertain embodiments, the security analytics system 118 may beimplemented as a distributed system. In these embodiments, the securityanalytics system 118 may span multiple information handling systems. Incertain embodiments, the security analytics system 118 may beimplemented in a cloud environment. In certain embodiments, the securityanalytics system 118 may be implemented in a virtual machine (VM)environment. In such embodiments, the VM environment may be configuredto dynamically and seamlessly scale the security analytics system 118 asneeded. Skilled practitioners of the art will recognize that many suchembodiments are possible. Accordingly, the foregoing is not intended tolimit the spirit, scope or intent of the invention.

In certain embodiments, an event stream collector 402 may be implementedto collect event and related contextual information, described ingreater detail herein, associated with various entity behaviors. Inthese embodiments, the method by which the event and contextualinformation is selected to be collected by the event stream collector402 is a matter of design choice. In certain embodiments, the event andcontextual information collected by the event stream collector 402 maybe processed by an enrichment module 406 to generate enriched entitybehavior information. In certain embodiments, the enrichment may includecertain contextual information related to a particular entity behavioror event. In certain embodiments, the enrichment may include certaintemporal information, such as timestamp information, related to aparticular entity behavior or event.

In certain embodiments, enriched entity behavior information may beprovided by the enrichment module 406 to a streaming 408 analyticsmodule. In turn, the streaming 408 analytics module may provide some orall of the enriched entity behavior information to an on-demand 410analytics module. As used herein, streaming 408 analytics broadly refersto analytics performed in near real-time on enriched entity behaviorinformation as it is received. Likewise, on-demand 410 analytics broadlyrefers herein to analytics performed, as they are requested, on enrichedentity behavior information after it has been received. In certainembodiments, the enriched entity behavior information may be associatedwith a particular event. In certain embodiments, the enrichment 406 andstreaming analytics 408 modules may be implemented to perform eventqueue analytics 404 operations, as described in greater detail herein.

In certain embodiments, the on-demand 410 analytics may be performed onenriched entity behavior associated with a particular interval of, orpoint in, time. In certain embodiments, the streaming 408 or on-demand410 analytics may be performed on enriched entity behavior associatedwith a particular user, group of users, one or more non-user entities,or a combination thereof. In certain embodiments, the streaming 408 oron-demand 410 analytics may be performed on enriched entity behaviorassociated with a particular resource, such as a facility, system,datastore, or service. Those of skill in the art will recognize thatmany such embodiments are possible. Accordingly, the foregoing is notintended to limit the spirit, scope or intent of the invention.

In certain embodiments, the results of various analytics operationsperformed by the streaming 408 or on-demand 410 analytics modules may beprovided to a storage Application Program Interface (API) 414. In turn,the storage API 412 may be implemented to provide access to variousdatastores ‘1’ 416 through ‘n’ 418, which in turn are used to store theresults of the analytics operations. In certain embodiments, thesecurity analytics system 118 may be implemented with a logging andreporting front-end 412, which is used to receive the results ofanalytics operations performed by the streaming 408 analytics module. Incertain embodiments, the datastores ‘1’ 416 through ‘n’ 418 mayvariously include a datastore of entity identifiers, temporal events, ora combination thereof.

In certain embodiments, the security analytics system 118 may include arisk scoring 420 module implemented to perform risk scoring operations,described in greater detail herein. In certain embodiments,functionalities of the risk scoring 420 module may be provided in theform of a risk management service 422. In certain embodiments, the riskmanagement service 422 may be implemented to perform operationsassociated with defining and managing an adaptive trust profile (ATP),as described in greater detail herein. In certain embodiments, the riskmanagement service 422 may be implemented to perform operationsassociated with detecting entity behavior that may be of analyticutility and adaptively responding to mitigate risk, as described ingreater detail herein. In certain embodiments, the risk managementservice 422 may be implemented to provide the results of variousanalytics operations performed by the streaming 406 or on-demand 408analytics modules. In certain embodiments, the risk management service422 may be implemented to use the storage API 412 to access variousenhanced cyber behavior and analytics information stored on thedatastores ‘1’ 414 through ‘n’ 416. Skilled practitioners of the artwill recognize that many such embodiments are possible. Accordingly, theforegoing is not intended to limit the spirit, scope or intent of theinvention.

FIG. 5 is a simplified block diagram of the operation of a securityanalytics system implemented in accordance with an embodiment of theinvention. In certain embodiments, the security analytics system 118 maybe implemented to perform operations associated with detecting entitybehavior that may be of analytic utility, as described in greater detailherein. In certain embodiments, the security analytics system 118 may beimplemented in combination with one or more endpoint agents 306, one ormore edge devices 202, various cloud services 206, and a network 140 toperform such operations.

In certain embodiments, the network edge device 202 may be implementedin a bridge, a firewall, or a passive monitoring configuration. Incertain embodiments, the edge device 202 may be implemented as softwarerunning on an information handling system. In certain embodiments, thenetwork edge device 202 may be implemented to provide integratedlogging, updating and control. In certain embodiments, the edge device202 may be implemented to receive network requests and context-sensitiveuser behavior information in the form of enriched user behaviorinformation 510, described in greater detail herein, from an endpointagent 306, likewise described in greater detail herein.

In certain embodiments, the security analytics system 118 may beimplemented as both a source and a sink of user behavior information. Incertain embodiments, the security analytics system 118 may beimplemented to serve requests for user/resource risk data. In certainembodiments, the edge device 202 and the endpoint agent 306,individually or in combination, may provide certain entity behaviorinformation to the security analytics system 118 using either push orpull approaches familiar to skilled practitioners of the art.

As described in greater detail herein, the edge device 202 may beimplemented in certain embodiments to receive enriched user behaviorinformation 510 from the endpoint agent 306. It will be appreciated thatsuch enriched user behavior information 510 will likely not be availablefor provision to the edge device 202 when an endpoint agent 306 is notimplemented for a corresponding endpoint device 304. However, the lackof such enriched user behavior information 510 may be accommodated invarious embodiments, albeit with reduced functionality related tooperations associated with defining and managing an entity profile,detecting entity behavior that may be normal or of analytic utility,mitigating associated risk, or a combination thereof.

In certain embodiments, a given user behavior may be enriched by anassociated endpoint agent 306 attaching contextual information to arequest. In certain embodiments, the context is embedded within anetwork request, which is then provided as enriched user behaviorinformation 510. In certain embodiments, the contextual information maybe concatenated, or appended, to a request, which in turn may beprovided as enriched user behavior information 510. In theseembodiments, the enriched user behavior information 510 may be unpackedupon receipt and parsed to separate the request and its associatedcontextual information. Certain embodiments of the invention reflect anappreciation that one possible disadvantage of such an approach is thatit may perturb certain Intrusion Detection System and/or IntrusionDetection Prevention (IDS/IDP) systems implemented on a network 140.

In certain embodiments, new flow requests may be accompanied by acontextual information packet sent to the edge device 202. In theseembodiments, the new flow requests may be provided as enriched userbehavior information 510. In certain embodiments, the endpoint agent 306may also send updated contextual information to the edge device 202 onceit becomes available. As an example, an endpoint agent 306 may share alist of files that have been read by a current process at any point intime once the information has been collected. To continue the example,such a list of files may be used to determine which data the endpointagent 306 may be attempting to exfiltrate.

In certain embodiments, point analytics processes executing on the edgedevice 202 may request a particular service. As an example, risk scoresassociated with a particular event on a per-user basis may be requested.In certain embodiments, the service may be requested from the securityanalytics system 118. In certain embodiments, the service may berequested from various cloud services 206.

In certain embodiments, contextual information associated with aparticular entity behavior may be attached to various network servicerequests. In certain embodiments, the request may be wrapped and thenhandled by proxy. In certain embodiments, a small packet of contextualinformation associated with an entity behavior may be sent with aservice request. In certain embodiments, service requests may be relatedto Domain Name Service (DNS), web browsing activity, email, and soforth, all of which are essentially requests for service by an endpointdevice 304. In certain embodiments, such service requests may beassociated with temporal event information, described in greater detailherein. Consequently, such requests can be enriched by the addition ofentity behavior contextual information (e.g., UserAccount,interactive/automated, data-touched, temporal event information, etc.).Accordingly, the edge device 202 can then use this information to managethe appropriate response to submitted requests.

In certain embodiments, the security analytics system 118 may beimplemented in different operational configurations. In certainembodiments, the security analytics system 118 may be implemented byusing the endpoint agent 306. In certain embodiments, the securityanalytics system 118 may be implemented by using endpoint agent 306 incombination with the edge device 202. In certain embodiments, the cloudservices 206 may likewise be implemented for use by the endpoint agent306, the edge device 202, and the security analytics system 118,individually or in combination. In these embodiments, the securityanalytics system 118 may be primarily oriented to performing riskassessment operations related to entity actions, software programactions, data accesses, or a combination thereof. In certainembodiments, software program actions may be treated as a proxy for theentity.

In certain embodiments, the endpoint agent 306 may be implemented toupdate the security analytics system 118 with user behavior andassociated contextual information, thereby allowing an offload ofcertain analytics processing overhead. In certain embodiments, thisapproach allows for longitudinal risk scoring, which assesses riskassociated with certain user behavior during a particular interval oftime. In certain embodiments, the security analytics system 118 may beimplemented to access risk scores associated with the same user account,but accrued on different endpoint devices 304. It will be appreciatedthat such an approach may prove advantageous when an adversary is“moving sideways” through a network environment, using differentendpoint devices 304 to collect information.

In certain embodiments, the security analytics system 118 may beprimarily oriented to applying risk mitigations in a way that maximizessecurity effort return-on-investment (ROI). In certain embodiments, thisapproach may be accomplished by providing additional contextual andentity behavior information associated with entity requests. As anexample, a web gateway may not concern itself with why a particular fileis being requested by a certain entity at a particular point in time.Accordingly, if the file cannot be identified as malicious or harmless,there is no context available to determine how, or if, to proceed. Toextend the example, the edge device 202 and security analytics system118 may be coupled such that requests can be contextualized and fittedinto a framework that evaluates their associated risk. Certainembodiments of the invention reflect an appreciation that such anapproach works well with web-based data loss protection (DLP)approaches, as each transfer is no longer examined in isolation, but inthe broader context of an identified entity's actions, at a particulartime, on the network 140.

As another example, the security analytics system 118 may be implementedto perform risk scoring processes to decide whether to block or allowunusual flows. In various embodiments, the risk scoring processes may beimplemented to include certain aspects of eXtensible Access ControlMarkup Language (XACML) approaches known to skilled practitioners of theart. In certain embodiments, XACML obligations may be implemented toblock or allow unusual flows. In certain embodiments, an XACMLobligation may be implemented as a directive from a policy decisionpoint (PDP) to a policy enforcement point (PEP) regarding what must beperformed before or after a flow is approved. Certain embodiments of theinvention reflect an appreciation that such an approach is highlyapplicable to defending against point-of-sale (POS) malware, a breachtechnique that has become increasingly more common in recent years.Certain embodiments of the invention likewise reflect an appreciationthat while various edge device 202 implementations may not stop all suchexfiltrations, they may be able to complicate the task for the attacker.

In certain embodiments, the security analytics system 118 may beprimarily oriented to maximally leverage contextual informationassociated with various entity behaviors within the system. In certainembodiments, data flow tracking is performed by one or more endpointagents 306, which allows the quantity and type of information associatedwith particular hosts to be measured. In turn, this information may beused to determine how the edge device 202 handles requests. Bycontextualizing such entity behavior on the network 140, the securityanalytics system 118 can provide intelligent protection, makingdecisions that make sense in the broader context of an organization'sactivities. Certain embodiments of the invention reflect an appreciationthat one advantage to such an approach is that information flowingthrough an organization, and the networks they employ, should betrackable, and substantial data breaches preventable. Skilledpractitioners of the art will recognize that many such embodiments andexamples are possible. Accordingly, the foregoing is not intended tolimit the spirit, scope or intent of the invention.

FIG. 6 is a simplified block diagram of an adaptive trust profileimplemented in accordance with an embodiment of the invention. As usedherein, an adaptive trust profile (ATP) 640 broadly refers to acollection of information that uniquely describes an entity's identityand their associated behavior, whether the behavior occurs within aphysical realm or cyberspace. In certain embodiments, an ATP may be usedto adaptively draw inferences regarding the trustworthiness of anentity. In certain embodiments, as described in greater detail herein,the drawing of the inferences may involve comparing a new entitybehavior to known past behaviors enacted by the entity. In certainembodiments, new entity behavior of analytic utility may represententity behavior that represents a security risk. As likewise usedherein, an entity broadly refers to something that exists as itself,whether physically or abstractly. In certain embodiments, an entity maybe a user entity, a non-user entity, or a combination thereof. Incertain embodiments, the identity of an entity may be known or unknown.

As used herein, a user entity broadly refers to an entity capable ofenacting a user behavior, as described in greater detail herein.Examples of a user entity include an individual person, a group ofpeople, an organization, or a government. As likewise used herein, anon-user entity broadly refers to an entity whose identity can bedescribed and may exhibit certain behavior, but is incapable of enactinga user behavior. Examples of a non-user entity include an item, adevice, such as endpoint and edge devices, a network, an account, adomain, an operation, and a process. Other examples of a non-user entityinclude a resource, such as a geographical location or formation, aphysical facility, a venue, a system, a software application, a datastore, and a service, such as a service operating in a cloudenvironment.

Certain embodiments of the invention reflect an appreciation that beingable to uniquely identity a device may assist in establishing whether ornot a particular login is legitimate. As an example, user impersonationsmay not occur at the user's endpoint, but rather, from another device orsystem. Certain embodiments of the invention likewise reflect anappreciation that profiling the behavior of a particular device orsystem may assist in determining whether or not it is actingsuspiciously.

In certain embodiments, an account may be local account, which runs on asingle machine. In certain embodiments, an account may be a globalaccount, providing access to multiple resources. In certain embodiments,a process may be implemented to run in an unattended mode, such as whenbacking up files or checking for software updates. Certain embodimentsof the invention reflect an appreciation that it is often advantageousto track events at the process level as a method of determining whichevents are associated with background processes and which are initiatedby a user entity.

In certain embodiments, an ATP 640 may be implemented to include a userentity profile 602, an associated user entity mindset profile 632, anon-user entity profile 634, and an entity state 638. As used herein, auser entity profile 602 broadly refers to a collection of informationthat uniquely describes a user entity's identity and their associatedbehavior, whether the behavior occurs within a physical realm orcyberspace. In certain embodiments, as described in greater detailherein, the user entity profile 602 may include user profile attributes612, user behavior factors 614, user mindset factors 626, or acombination thereof. In certain embodiments, the user profile attributes612 may include certain user authentication factors 604, described ingreater detail herein, and personal information 608.

As used herein, a user profile attribute 612 broadly refers to data ormetadata that can be used, individually or in combination with otheruser profile attributes 612, user behavior factors 614, or user mindsetfactors 626, to ascertain the identity of a user entity. In variousembodiments, certain user profile attributes 612 may be uniquelyassociated with a particular user entity. In certain embodiments, thepersonal information 608 may include non-sensitive personal informationassociated with a user entity, such as their name, title, position,role, and responsibilities. In certain embodiments, the personalinformation 608 may likewise include technical skill level information,peer information, expense account information, paid time off (PTO)information, data analysis information, insider information,misconfiguration information, third party information, or a combinationthereof. In certain embodiments, the personal information 608 maycontain sensitive personal information associated with a user entity. Asused herein, sensitive personal information (SPI), also commonlyreferred to as personally identifiable information (PII), broadly refersto any information usable to ascertain the identity of a user entity,either by itself, or in combination with other information, such ascontextual information described in greater detail herein.

Examples of SPI may include the full or legal name of a user entity,initials or nicknames, place and date of birth, home and businessaddresses, personal and business telephone numbers, their gender, andother genetic information. Additional examples of SPI may includegovernment-issued identifiers, such as a Social Security Number (SSN) ora passport number, vehicle registration plate and serial numbers, anddriver's license numbers. Other examples of SPI may include certainemail addresses and social media identifiers, credit and debit cardnumbers, and other digital identity information. Yet other examples ofSPI may include employer-issued identifiers, financial transactioninformation, credit scores, electronic medical records (EMRs), insuranceclaim information, personal correspondence, and so forth. Furtherexamples of SPI may include user authentication factors 604, such asbiometrics, user identifiers and passwords, and personal identificationnumbers (PINs).

In certain embodiments, the SPI may include information considered by anindividual user, a group of users, or an organization (e.g., a company,a government or non-government organization, etc.), to be confidentialor proprietary. One example of such confidential information isprotected health information (PHI). As used herein, PHI broadly refersto any information associated with the health status, provision ofhealth care, or payment for health care that is created or collected bya “covered entity,” or an associate thereof, that can be linked to aparticular individual. As used herein, a “covered entity” broadly refersto health plans, healthcare clearinghouses, healthcare providers, andothers, who may electronically communicate any health-relatedinformation associated with a particular individual. Examples of suchPHI may include any part of a patient's medical record, healthcarerecord, or payment history for medical or healthcare services.

As used herein, a user behavior factor 614 broadly refers to informationassociated with a user entity's behavior, whether the behavior occurswithin a physical realm or cyberspace. In certain embodiments, userbehavior factors 614 may include the user entity's access rights 616,the user entity's interactions 618, and the date/time/frequency 620 ofwhen the interactions 618 are enacted. In certain embodiments, the userbehavior factors 614 may likewise include the user entity's location622, and the gestures 624 used by the user entity to enact theinteractions 618.

In certain embodiments, the user entity gestures 624 may include keystrokes on a keypad, a cursor movement, a mouse movement or click, afinger swipe, tap, or other hand gesture, an eye movement, or somecombination thereof. In certain embodiments, the user entity gestures624 may likewise include the cadence of the user's keystrokes, themotion, force and duration of a hand or finger gesture, the rapidity anddirection of various eye movements, or some combination thereof. Incertain embodiments, the user entity gestures 624 may include variousaudio or verbal commands performed by the user.

As used herein, user mindset factors 626 broadly refer to informationused to make inferences regarding the mental state of a user entity at aparticular point in time, during the occurrence of an event or anenactment of a user behavior, or a combination thereof. As likewise usedherein, mental state broadly refers to a hypothetical statecorresponding to the way a user entity may be thinking or feeling.Likewise, as used herein, an event broadly refers to the occurrence ofaction performed by an entity. In certain embodiments, the user entitymindset factors 626 may include a personality type 628. Examples ofknown approaches for determining a personality type 628 include Jungiantypes, Myers-Briggs type indicators, Keirsy Temperament Sorter,Socionics, Enneagram of Personality, and Eyseneck's three-factor model.

In certain embodiments, the user mindset factors 626 may include variousbehavioral biometrics 630. As used herein, a behavioral biometric 626broadly refers to a physiological indication of a user entity's mentalstate. Examples of behavioral biometrics 630 may include a user entity'sblood pressure, heart rate, respiratory rate, eye movements and irisdilation, facial expressions, body language, tone and pitch of voice,speech patterns, and so forth.

Certain embodiments of the invention reflect an appreciation thatcertain user behavior factors 614, such as user entity gestures 624, mayprovide additional information related to inferring a user entity'smental state. As an example, a user entering text at a quick pace with arhythmic cadence may indicate intense focus. Likewise, an individualuser intermittently entering text with forceful keystrokes may indicatethe user is in an agitated state. As another example, the user mayintermittently enter text somewhat languorously, which may indicatebeing in a thoughtful or reflective state of mind. As yet anotherexample, the user may enter text with a light touch with an unevencadence, which may indicate the user is hesitant or unsure of what isbeing entered.

Certain embodiments of the invention likewise reflect an appreciationthat while the user entity gestures 624 may provide certain indicationsof the mental state of a particular user entity, they may not providethe reason for the user entity to be in a particular mental state.Likewise, certain embodiments of the invention include an appreciationthat certain user entity gestures 624 and behavioral biometrics 630 arereflective of an individual user's personality type 628. As an example,aggressive, forceful keystrokes combined with an increased heart ratemay indicate normal behavior for a particular user when composingend-of-month performance reviews. In various embodiments, certain userentity behavior factors 614, such as user gestures 624, may becorrelated with certain contextual information, as described in greaterdetail herein.

In certain embodiments, a security analytics system 118, described ingreater detail herein, may be implemented to include an adaptive trustprofile (ATP) system 120. In certain embodiments, the ATP system 120 maybe implemented to use a user entity profile 602 in combination with anentity state 638 to generate a user entity mindset profile 632. As usedherein, entity state 638 broadly refers to the context of a particularevent or entity behavior. In certain embodiments, the entity state 638may be a long-term entity state or a short-term entity state. As usedherein, a long-term entity state 638 broadly relates to an entity state638 that persists for an extended interval of time, such as six monthsor a year. As likewise used herein, a short-term entity state 638broadly relates to an entity state 638 that occurs for a brief intervalof time, such as a few minutes or a day. In various embodiments, themethod by which an entity state's 638 associated interval of time isconsidered to be long-term or short-term is a matter of design choice.

As an example, a particular user may have a primary work location, suchas a branch office, and a secondary work location, such as theircompany's corporate office. In this example, the user's primary andsecondary offices respectively correspond to the user's location 622,whereas the presence of the user at either office corresponds to anentity state 638. To continue the example, the user may consistentlywork at their primary office Monday through Thursday, but at theircompany's corporate office on Fridays. To further continue the example,the user's presence at their primary work location may be a long-termentity state 638, while their presence at their secondary work locationmay be a short-term entity state 638. Accordingly, a date/time/frequency620 user entity behavior factor 614 can likewise be associated with userbehavior respectively enacted on those days, regardless of theircorresponding locations. Consequently, the long-term user entity state638 on Monday through Thursday will typically be “working at the branchoffice” and the short-term entity state 638 on Friday will likely be“working at the corporate office.”

As likewise used herein, a user entity mindset profile 632 broadlyrefers to a collection of information that reflects an inferred mentalstate of a user entity at a particular time during the occurrence of anevent or an enactment of a user behavior. As an example, certaininformation may be known about a user entity, such as their name, theirtitle and position, and so forth, all of which are user profileattributes 612. Likewise, it may be possible to observe a user entity'sassociated user behavior factors 614, such as their interactions withvarious systems, when they log-in and log-out, when they are active atthe keyboard, the rhythm of their keystrokes, and which files theytypically use.

Certain embodiments of the invention reflect an appreciation thesebehavior factors 614 can be considered to be a behavioral fingerprint.In certain embodiments, the user behavior factors 614 may change, alittle or a lot, from day to day. These changes may be benign, such aswhen a user entity begins a new project and accesses new data, or theymay indicate something more concerning, such as a user entity who isactively preparing to steal data from their employer. In certainembodiments, the user behavior factors 614 may be implemented toascertain the identity of a user entity. In certain embodiments, theuser behavior factors 614 may be uniquely associated with a particularentity.

In certain embodiments, observed user behaviors may be used to build auser entity profile 602 for a particular user or other entity. Inaddition to creating a model of a user's various attributes and observedbehaviors, these observations can likewise be used to infer things thatare not necessarily explicit. Accordingly, in certain embodiments, abehavioral fingerprint may be used in combination with an ATP 640 togenerate an inference regarding an associated user entity. As anexample, a particular user may be observed eating a meal, which may ormay not indicate the user is hungry. However, if it is also known thatthe user worked at their desk throughout lunchtime and is now eating asnack during a mid-afternoon break, then it can be inferred they areindeed hungry.

As likewise used herein, a non-user entity profile 634 broadly refers toa collection of information that uniquely describes a non-user entity'sidentity and their associated behavior, whether the behavior occurswithin a physical realm or cyberspace. In various embodiments, thenon-user entity profile 634 may be implemented to include certainnon-user profile attributes 636. As used herein, a non-user profileattribute 636 broadly refers to data or metadata that can be used,individually or in combination with other non-user profile attributes636, to ascertain the identity of a non-user entity. In variousembodiments, certain non-user profile attributes 636 may be uniquelyassociated with a particular non-user entity.

In certain embodiments, the non-user profile attributes 636 may beimplemented to include certain identity information, such as a non-userentity's network, Media Access Control (MAC), or physical address, itsserial number, associated configuration information, and so forth. Invarious embodiments, the non-user profile attributes 636 may beimplemented to include non-user behavior information associated withinteractions between certain user and non-user entities, the type ofthose interactions, the data exchanged during the interactions, thedate/time/frequency of such interactions, and certain services accessedor provided.

In certain embodiments, the ATP system 120 may be implemented to includean event enrichment 680 module, an analytic utility detection 682module, a contextualization 684 module, and a meaning derivation 686module, or a combination thereof. In various embodiments, the eventenrichment 680 module may be implemented to perform certain eventenrichment operations, described in greater detail herein. In variousembodiments, the analytic utility detection 682 module may beimplemented to perform certain analytic utility detection operations, aslikewise described in greater detail herein. In various embodiments, asdescribed in greater detail herein, the contextualization 684 module maybe implemented to perform certain contextualization operations. Aslikewise described in greater detail herein, the meaning derivation 686module may be implemented to perform certain meaning derivationoperations. In various embodiments, the event enrichment 680 module, themeaning derivation 686 module, the contextualization 684 module, and theanalytic utility detection 686 module provide an ATP referencearchitecture for performing various ATP operations, described in greaterdetail herein.

In various embodiments, as described in greater detail herein, the ATPsystem 120 may be implemented to use certain data associated with an ATP640 to derive an inference for contextualizing anelectronically-observable behavior of a corresponding entity. In certainembodiments, the ATP system 120 may be implemented to use a user profile602 in combination with a user entity mindset profile 634 and anassociated entity state 638 to infer a user entity's intent. In certainembodiments, the ATP system 120 may be implemented to use various datastored in a repository of ATP data 670 to perform such an inference. Incertain embodiments, the repository of ATP data 670 may include variousATPs 640 and associated contextual information, described in greaterdetail herein.

In various embodiments, the ATP system 120 may be implemented to usecertain data associated with an ATP 640 to provide a probabilisticmeasure of whether a particular electronically-observable event is ofanalytic utility. In certain embodiments, an electronically-observableevent that is of analytic utility may be determined to be anomalous,abnormal, unexpected, or malicious. To continue the prior example, auser may typically work out of their company's corporate office onFridays. Furthermore, various mindset factors 626 within theirassociated user entity profile 602 may indicate that the user istypically relaxed and methodical when working with customer data.Moreover, the user's user entity profile 602 indicates that suchinteractions 618 with customer data typically occur on Monday morningsand the user rarely, if ever, copies or downloads customer data.However, the user may decide to interact with certain customer data lateat night, on a Friday, while in their company's corporate office. Asthey do so, they exhibit an increased heart rate, rapid breathing, andfurtive keystrokes while downloading a subset of customer data to aflash drive.

Consequently, their user entity mindset profile 632 may reflect anervous, fearful, or guilty mindset, which is inconsistent with theentity state 638 of dealing with customer data in general. Moreparticularly, downloading customer data late at night on a day the useris generally not in their primary office results in an entity state 638that is likewise inconsistent with the user's typical user behavior. Asa result, the ATP system 120 may infer that the user's behavior mayrepresent a security threat. Those of skill in the art will recognizethat many such embodiments and examples are possible. Accordingly, theforegoing is not intended to limit the spirit, scope or intent of theinvention.

FIG. 7 is simplified block diagram of process flows associated with theoperation of an adaptive trust profile (ATP) system implemented inaccordance with an embodiment of the invention. In certain embodiments,the ATP system 120 may be implemented to define and manage an ATP 640,as described in greater detail herein. In certain embodiments, the ATP640 may be implemented to comprise a user entity profile 602, likewisedescribed in greater detail herein. In certain embodiments, the ATPsystem 120 may be implemented use the resulting user entity profile 602in combination with a particular entity state 638 to generate a userentity mindset profile 632, likewise described in greater detail herein.In certain embodiments, the ATP system 120 may be implemented to use theresulting user entity mindset profile 632 in combination with anassociated user entity profile 602, non-user entity profile 634, andentity state 638 to detect entity behavior of analytic utility.

In certain embodiments, the ATP system 120 may be implemented to processcertain entity information associated with defining and managing an ATP640. As used herein, entity information broadly refers to informationassociated with a particular entity. In various embodiments, the entityinformation may include certain types of content. In certainembodiments, such content may include text, unstructured data,structured data, graphical images, photographs, audio recordings, videorecordings, biometric information, and so forth. In certain embodiments,the entity information may include metadata. In certain embodiments, themetadata may include entity attributes, which in turn may includecertain entity identifier types or classifications.

In various embodiments, the ATP system 120 may be implemented to usecertain entity identifier information to ascertain the identity of anassociated entity at a particular point in time. As used herein, entityidentifier information broadly refers to an information elementassociated with an entity that can be used to ascertain or corroboratethe identity of its corresponding entity at a particular point in time.In certain embodiments, the entity identifier information may includeuser authentication factors, user profile attributes, user behaviorfactors, user mindset factors, information associated with variousendpoint and edge devices, networks, resources, or a combinationthereof.

In certain embodiments, the entity identifier information may includetemporal information. As used herein, temporal information broadlyrefers to a measure of time (e.g., a date, timestamp, etc.), a measureof an interval of time (e.g., a minute, hour, day, etc.), or a measureof an interval of time (e.g., two consecutive weekdays days, or betweenJun. 3, 2017 and Mar. 4, 2018, etc.). In certain embodiments, thetemporal information may be associated with an event associated with aparticular point in time. As used herein, such a temporal event broadlyrefers to an occurrence, action or activity enacted by, or associatedwith, an entity at a particular point in time.

Examples of such temporal events include making a phone call, sending atext or an email, using a device, such as an endpoint device, accessinga system, and entering a physical facility. Other examples of temporalevents include uploading, transferring, downloading, modifying, ordeleting data, such as data stored in a datastore, or accessing aservice. Yet other examples of temporal events include interactionsbetween two or more users, interactions between a user and a device,interactions between a user and a network, and interactions between auser and a resource, whether physical or otherwise. Yet still otherexamples of temporal events include a change in name, address, physicallocation, occupation, position, role, marital status, gender,association, affiliation, or assignment.

As likewise used herein, temporal event information broadly refers totemporal information associated with a particular event. In variousembodiments, the temporal event information may include certain types ofcontent. In certain embodiments, such types of content may include text,unstructured data, structured data, graphical images, photographs, audiorecordings, video recordings, and so forth. In certain embodiments, theentity information may include metadata. In various embodiments, themetadata may include temporal event attributes, which in turn mayinclude certain entity identifier types or classifications, described ingreater detail herein.

In certain embodiments, the ATP system 120 may be implemented to useinformation associated with such temporal resolution of an entity'sidentity to assess the risk associated with a particular entity, at aparticular point in time, and adaptively respond with an associatedresponse. In certain embodiments, the ATP system 120 may be implementedto respond to such assessments in order to reduce operational overheadand improve system efficiency while maintaining security integrity. Incertain embodiments, the response to such assessments may be performedby a security administrator. Accordingly, certain embodiments of theinvention may be directed towards assessing the risk associated with theaffirmative resolution of the identity of an entity at a particularpoint in time in combination with its associated contextual information.Consequently, the ATP system 120 may be more oriented in variousembodiments to risk adaptation than to security administration.

In certain embodiments, ATP 640 definition and management operations arebegun with the receipt of information associated with event i 706. Incertain embodiments, information associated with an initial event i 706may include user profile attributes, user behavior factors, user mindsetfactors, entity state information, contextual information, described ingreater detail herein, or a combination thereof. In various embodiments,certain user entity profile 602, user entity mindset profile 632,non-user entity profile 634, and entity state 638 data stored in arepository of ATP data 670 may be retrieved and then used to performevent enrichment 712 operations to enrich the information associatedwith event i 706. In certain embodiment, event enrichment 712 operationsare performed by the event enrichment module 680 of the ATP system 120.Analytic utility detection 714 operations are then performed on theresulting enriched information associated with event i 706 to determinewhether it is of analytic utility. In certain embodiments, analyticutility detection 714 operations are performed by the analytic utilitydetection module 682 of the ATP system 120.

In various embodiments, certain contextualization information stored inthe repository of ATP data 670 may be retrieved and then used to performcontextualization 716 operations to provide context, based upon theentity's user entity profile 602 or non-user entity profile 634, and itsassociated entity state 638. In certain embodiments, contextualization716 operations are performed by the contextualization module 684 of theATP system 120. In certain embodiments, meaning derivation 718operations are then performed on the contextualized informationassociated with event i 706 to derive meaning. In certain embodiments,meaning derivation 718 operations are performed by the meaningderivation module 686 of the ATP system. In certain embodiments, thederivation of meaning may include inferring the intent of an entityassociated with event i 706. In certain embodiments, the resultinginformation associated with event i 706 is then used to update the userentity profile 602 or non-user entity profile 634 corresponding to theentity associated with event i 706. In certain embodiments, the processis iteratively repeated, proceeding with information associated withevent i+1 708 through event i+n 710.

From the foregoing, skilled practitioners of the art will recognize thata user entity profile 602, or a non-user entity profile 634, or the twoin combination, as implemented in certain embodiments, not only allowsthe identification of events associated with a particular entity thatmay be of analytic utility, but also provides higher-level data thatallows for the contextualization of observed events. Accordingly, byviewing individual sets of events both in context and with a view to howthey may be of analytic utility, it is possible to achieve a morenuanced and higher-level comprehension of an entity's intent.

FIGS. 8a and 8b show a block diagram of a security analytics environmentimplemented in accordance with an embodiment of the invention. Incertain embodiments, analyses performed by a security analytics system118 may be used to identify behavior associated with a particular entitythat may be of analytic utility. In certain embodiments, the entitybehavior of analytic utility may be identified at a particular point intime, during the occurrence of an event, the enactment of a user ornon-user behavior, or a combination thereof.

As used herein, an entity broadly refers to something that exists asitself, whether physically or abstractly. In certain embodiments, anentity may be a user entity, a non-user entity, or a combinationthereof. In certain embodiments, a user entity may be an individualuser, such as user ‘A’ 802 or ‘B’ 872, a group, an organization, or agovernment. In certain embodiments, a non-user entity may likewise be anitem, a device, such as endpoint 304 and edge 202 devices, a network,such as an internal 844 and external 846 networks, a domain, anoperation, or a process. In certain embodiments, a non-user entity maybe a resource 850, such as a geographical location or formation, aphysical facility 852, such as a venue, various physical securitydevices 854, a system 856, shared devices 858, such as printer, scanner,or copier, a data store 860, or a service 862, such as a service 862operating in a cloud environment.

As likewise used herein, an event broadly refers to the occurrence of anaction performed by an entity. In certain embodiments, the action may bedirectly associated with a user behavior, described in greater detailherein. As an example, a first user may attach a binary file infectedwith a virus to an email that is subsequently sent to a second user. Inthis example, the act of attaching the binary file to the email isdirectly associated with a user behavior enacted by the first user. Incertain embodiments, the action may be indirectly associated with a userbehavior. To continue the example, the recipient of the email may openthe infected binary file, and as a result, infect their computer withmalware. To further continue the example, the act of opening theinfected binary file is directly associated with a user behavior enactedby the second user. However, the infection of the email recipient'scomputer by the infected binary file is indirectly associated with thedescribed user behavior enacted by the second user.

In various embodiments, certain user authentication factors 604 may beused to authenticate the identity of a user entity. In certainembodiments, the user authentication factors 604 may be used to ensurethat a particular user, such as user ‘A’ 802 or ‘B’ 872, is associatedwith their corresponding user entity profile, rather than a user entityprofile associated with another user. In certain embodiments, the userauthentication factors 604 may include a user's biometrics 606 (e.g., afingerprint or retinal scan), tokens 608 (e.g., a dongle containingcryptographic keys), user identifiers and passwords (ID/PW) 610, andpersonal identification numbers (PINs).

In certain embodiments, information associated with such user behaviormay be stored in a user entity profile, described in greater detailherein. In certain embodiments, the user entity profile may be stored ina repository of adaptive trust profile (ATP) data 670. In certainembodiments, as likewise described in greater detail herein, the userentity profile may include user profile attributes 612, user behaviorfactors 614, user mindset factors 626, or a combination thereof. As usedherein, a user profile attribute 612 broadly refers to data or metadatathat can be used, individually or in combination with other user profileattributes 612, user behavior factors 614, or user mindset factors 626,to ascertain the identity of a user entity. In various embodiments,certain user profile attributes 612 may be uniquely associated with aparticular user entity.

As likewise used herein, a user behavior factor 614 broadly refers toinformation associated with a user's behavior, whether the behavioroccurs within a physical realm or cyberspace. In certain embodiments,the user behavior factors 614 may include the user's access rights 616,the user's interactions 618, and the date/time/frequency 620 of thoseinteractions 618. In certain embodiments, the user behavior factors 614may likewise include the user's location 622 when the interactions 618are enacted, and the user gestures 624 used to enact the interactions618.

In various embodiments, certain date/time/frequency 620 user behaviorfactors 614 may be implemented as ontological or societal time, or acombination thereof. As used herein, ontological time broadly refers tohow one instant in time relates to another in a chronological sense. Asan example, a first user behavior enacted at 12:00 noon on May 17, 2017may occur prior to a second user behavior enacted at 6:39 PM on May 18,2018. Skilled practitioners of the art will recognize one value ofontological time is to determine the order in which various userbehaviors have been enacted.

As likewise used herein, societal time broadly refers to the correlationof certain user profile attributes 612, user behavior factors 614, usermindset factors 626, or a combination thereof, to one or more instantsin time. As an example, user ‘A’ 802 may access a particular system 856to download a customer list at 3:47 PM on Nov. 3, 2017. Analysis oftheir user behavior profile indicates that it is not unusual for user‘A’ 802 to download the customer list on a weekly basis. However,examination of their user behavior profile also indicates that user ‘A’802 forwarded the downloaded customer list in an email message to user‘B’ 872 at 3:49 PM that same day. Furthermore, there is no record intheir user behavior profile that user ‘A’ 802 has ever communicated withuser ‘B’ 872 in the past. Moreover, it may be determined that user ‘B’872 is employed by a competitor. Accordingly, the correlation of user‘A’ 806 downloading the customer list at one point in time, and thenforwarding the customer list to user ‘B’ 872 at a second point in timeshortly thereafter, is an example of societal time.

In a variation of the prior example, user ‘A’ 802 may download thecustomer list at 3:47 PM on Nov. 3, 2017. However, instead ofimmediately forwarding the customer list to user ‘B’ 872, user ‘A’ 802leaves for a two week vacation. Upon their return, they forward thepreviously-downloaded customer list to user ‘B’ 872 at 9:14 AM on Nov.20, 2017. From an ontological time perspective, it has been two weekssince user ‘A’ 802 accessed the system 856 to download the customerlist. However, from a societal time perspective, they have stillforwarded the customer list to user ‘B’ 872, despite two weeks havingelapsed since the customer list was originally downloaded.

Accordingly, the correlation of user ‘A’ 802 downloading the customerlist at one point in time, and then forwarding the customer list to user‘B’ 872 at a much later point in time, is another example of societaltime. More particularly, it may be inferred that the intent of user ‘A’802 did not change during the two weeks they were on vacation.Furthermore, user ‘A’ 802 may have attempted to mask an intendedmalicious act by letting some period of time elapse between the timethey originally downloaded the customer list and when they eventuallyforwarded it to user ‘B’ 872. From the foregoing, those of skill in theart will recognize that the use of societal time may be advantageous indetermining whether a particular entity behavior is of analytic utility.As used herein, mindset factors 626 broadly refer to information used toinfer the mental state of a user at a particular point in time, duringthe occurrence of an event, an enactment of a user behavior, orcombination thereof.

In certain embodiments, the security analytics system 118 may beimplemented to process certain entity information, described in greaterdetail herein, associated with providing resolution of the identity ofan entity at a particular point in time. In various embodiments, thesecurity analytics system 118 may be implemented to use certain entityidentifier information, likewise described in greater detail herein, toascertain the identity of an associated entity at a particular point intime. In various embodiments, the entity identifier information mayinclude certain temporal information, described in greater detailherein. In certain embodiments, the temporal information may beassociated with an event associated with a particular point in time.

In certain embodiments, the security analytics system 118 may beimplemented to use information associated with certain user behaviorelements to resolve the identity of an entity at a particular point intime. A user behavior element, as used herein, broadly refers to adiscrete element of a user entity's behavior during the performance of aparticular operation in a physical realm, cyberspace, or a combinationthereof. In certain embodiments, such user behavior elements may beassociated with a user/device 830, a user/network 842, a user/resource848, a user/user 860 interaction, or a combination thereof.

As an example, user ‘A’ 802 may use an endpoint device 304 to browse aparticular web page on a news site on an external system 876. In thisexample, the individual actions performed by user ‘A’ 802 to access theweb page are user behavior elements that constitute a user behavior. Asanother example, user ‘A’ 802 may use an endpoint device 304 to downloada data file from a particular system 856. In this example, theindividual actions performed by user ‘A’ 802 to download the data file,including the use of one or more user authentication factors 604 foruser authentication, are user behavior elements that constitute a userbehavior. In certain embodiments, the user/device 830 interactions mayinclude an interaction between a user, such as user ‘A’ 802 or ‘13’ 872,and an endpoint device 304.

In certain embodiments, the user/device 830 interaction may includeinteraction with an endpoint device 304 that is not connected to anetwork at the time the interaction occurs. As an example, user ‘A’ 802or ‘13’ 872 may interact with an endpoint device 304 that is offline,using applications 832, accessing data 834, or a combination thereof, itmay contain. Those user/device 830 interactions, or their result, may bestored on the endpoint device 304 and then be accessed or retrieved at alater time once the endpoint device 304 is connected to the internal 844or external 846 networks. In certain embodiments, an endpoint agent 306may be implemented to store the user/device 830 interactions when theuser device 304 is offline.

In certain embodiments, an endpoint device 304 may be implemented with adevice camera 828. In certain embodiments, the device camera 828 may beintegrated into the endpoint device 304. In certain embodiments, thedevice camera 828 may be implemented as a separate device configured tointeroperate with the endpoint device 304. As an example, a webcamfamiliar to those of skill in the art may be implemented receive andcommunicate various image and audio signals to an endpoint device 304via a Universal Serial Bus (USB) interface.

In certain embodiments, the device camera 828 may be implemented tocapture and provide user/device 830 interaction information to anendpoint agent 306. In various embodiments, the device camera 828 may beimplemented to provide surveillance information related to certainuser/device 830 or user/user 870 interactions. In certain embodiments,the surveillance information may be used by the security analyticssystem 118 to detect behavior associated with a user entity, such asuser ‘A’ 802 or user ‘13’ 872 that may be of analytic utility.

In certain embodiments, the endpoint device 304 may be used tocommunicate data through the use of an internal network 844, an externalnetwork 846, or a combination thereof. In certain embodiments, theinternal 844 and the external 846 networks may include a public network,such as the Internet, a physical private network, a virtual privatenetwork (VPN), or any combination thereof. In certain embodiments, theinternal 844 and external 846 networks may likewise include a wirelessnetwork, including a personal area network (PAN), based on technologiessuch as Bluetooth. In various embodiments, the wireless network mayinclude a wireless local area network (WLAN), based on variations of theIEEE 802.11 specification, commonly referred to as WiFi. In certainembodiments, the wireless network may include a wireless wide areanetwork (WWAN) based on an industry standard including various 3G, 4Gand 5G technologies.

In certain embodiments, the user/user 870 interactions may includeinteractions between two or more user entities, such as user ‘A’ 802 and13′ 872. In certain embodiments, the user/user interactions 870 may bephysical, such as a face-to-face meeting, via a user/device 830interaction, a user/network 842 interaction, a user/resource 648interaction, or some combination thereof. In certain embodiments, theuser/user 870 interaction may include a face-to-face verbal exchange. Incertain embodiments, the user/user 870 interaction may include a writtenexchange, such as text written on a sheet of paper. In certainembodiments, the user/user 870 interaction may include a face-to-faceexchange of gestures, such as a sign language exchange.

In certain embodiments, temporal event information associated withvarious user/device 830, user/network 842, user/resource 848, oruser/user 870 interactions may be collected and used to providereal-time resolution of the identity of an entity at a particular pointin time. Those of skill in the art will recognize that many suchexamples of user/device 830, user/network 842, user/resource 848, anduser/user 870 interactions are possible. Accordingly, the foregoing isnot intended to limit the spirit, scope or intent of the invention.

In various embodiments, the security analytics system 118 may beimplemented to process certain contextual information in the performanceof certain security analytic operations. As used herein, contextualinformation broadly refers to any information, directly or indirectly,individually or in combination, related to a particular entity behavior.In certain embodiments, entity behavior may include a user entity'sphysical behavior, cyber behavior, or a combination thereof. As likewiseused herein, a user entity's physical behavior broadly refers to anyuser behavior occurring within a physical realm, such as speaking,gesturing, facial patterns or expressions, walking, and so forth. Moreparticularly, such physical behavior may include any action enacted byan entity user that can be objectively observed, or indirectly inferred,within a physical realm. In certain embodiments, the objectiveobservation, or indirect inference, of the physical behavior may beperformed electronically.

As an example, a user may attempt to use an electronic access card toenter a secured building at a certain time. In this example, the use ofthe access card to enter the building is the action and the reading ofthe access card makes the user's physical behaviorelectronically-observable. As another example, a first user mayphysically transfer a document to a second user, which is captured by avideo surveillance system. In this example, the physical transferal ofthe document from the first user to the second user is the action.Likewise, the video record of the transferal makes the first and seconduser's physical behavior electronically-observable. As used herein,electronically-observable user behavior broadly refers to any behaviorexhibited or enacted by a user entity that can be observed through theuse of an electronic device (e.g., an electronic sensor), a computingdevice or system (e.g., an endpoint 304 or edge 202 device, a physicalsecurity device 854, a system 856, a shared device 858, etc.), computerinstructions (e.g., a software application), or a combination thereof.

Cyber behavior, as used herein, broadly refers to any behavior occurringin cyberspace, whether enacted by an individual user, a group of users,or a system acting at the behest of an individual user, a group ofusers, or other entity. More particularly, cyber behavior may includephysical, social, or mental actions that can be objectively observed, orindirectly inferred, within cyberspace. As an example, a user may use anendpoint device 304 to access and browse a particular website on theInternet. In this example, the individual actions performed by the userto access and browse the website constitute a cyber behavior. As anotherexample, a user may use an endpoint device 304 to download a data filefrom a particular system 856 at a particular point in time. In thisexample, the individual actions performed by the user to download thedata file, and associated temporal information, such as a time-stampassociated with the download, constitute a cyber behavior. In theseexamples, the actions are enacted within cyberspace, in combination withassociated temporal information, which makes themelectronically-observable.

In certain embodiments, the contextual information may include locationdata 836. In certain embodiments, the endpoint device 304 may beconfigured to receive such location data 836, which is used as a datasource for determining the user's location 622. In certain embodiments,the location data 836 may include Global Positioning System (GPS) dataprovided by a GPS satellite 838. In certain embodiments, the locationdata 836 may include location data 836 provided by a wireless network,such as from a cellular network tower 840. In certain embodiments (notshown), the location data 836 may include various Internet Protocol (IP)or other network address information assigned to the endpoint 304 oredge 202 device. In certain embodiments (also not shown), the locationdata 836 may include recognizable structures or physical addresseswithin a digital image or video recording.

In certain embodiments, the endpoint devices 304 may include an inputdevice (not shown), such as a keypad, magnetic card reader, tokeninterface, biometric sensor, and so forth. In certain embodiments, suchendpoint devices 304 may be directly, or indirectly, connected to aparticular facility 852, physical security device 854, system 856, orshared device 858. As an example, the endpoint device 304 may bedirectly connected to an ingress/egress system, such as an electroniclock on a door or an access gate of a parking garage. As anotherexample, the endpoint device 304 may be indirectly connected to aphysical security device 854 through a dedicated security network.

In certain embodiments, the security analytics system 118 may beimplemented to perform various risk-adaptive protection operations.Risk-adaptive, as used herein, broadly refers to adaptively respondingto risks associated with an electronically-observable entity behavior.In various embodiments, the security analytics system 118 may beimplemented to perform certain risk-adaptive protection operations bymonitoring certain entity behaviors, assess the corresponding risk theymay represent, individually or in combination, and respond with anassociated response. In certain embodiments, such responses may be basedupon contextual information, described in greater detail herein,associated with a given entity behavior.

In certain embodiments, various risk-adaptive behavior factors 674,likewise described in greater detail herein, may be used to perform therisk-adaptive protection operations. In certain embodiments, therisk-adaptive behavior factors 674 may include user profile attributes612, user behavior factors 614, user mindset factors 626, or acombination thereof. In these embodiments, the risk-adaptive behaviorfactors 674 used to perform the risk-adaptive protection operations is amatter of design choice.

In certain embodiments, the security analytics system 118 may beimplemented as a stand-alone system. In certain embodiments, thesecurity analytics system 118 may be implemented as a distributedsystem. In certain embodiment, the security analytics system 118 may beimplemented as a virtual system, such as an instantiation of one or morevirtual machines (VMs). In certain embodiments, the security analyticssystem 118 may be implemented as a security analytics service 864. Incertain embodiments, the security analytics service 864 may beimplemented in a cloud environment familiar to those of skill in theart. In various embodiments, the security analytics system 118 may usedata stored in a repository of security analytics data 880 in theperformance of certain security analytics operations, described ingreater detail herein. Those of skill in the art will recognize thatmany such embodiments are possible. Accordingly, the foregoing is notintended to limit the spirit, scope or intent of the invention.

FIG. 9 shows a functional block diagram of an adaptive trust profile(ATP) system implemented in accordance with an embodiment of theinvention. In various embodiments, certain ATP-related information,described in greater detail herein, may be provided by endpoint devices304, edge devices 202, and third party sources 906. In certainembodiments, the receipt of ATP information provided by third partysources 906 may be facilitated through the implementation of one or moreApache NiFi connectors 908, familiar to skilled practitioners of theart. In certain embodiments, sessionalization operations are performedin block 910 on the ATP-related information provided by the endpointdevices 304, edge devices 202, and third party sources 906 to generatediscrete sessions. In these embodiments, the method by which ATP-relatedinformation is selected to be used in the generation of a particularsession, and the method by which the session is generated, is a matterof design choice.

As used herein, a session broadly refers to an interval of time duringwhich one or more user or non-user behaviors are respectively enacted bya user or non-user entity. In certain embodiments, the user or non-userbehaviors enacted during a session may be respectively associated withone or more events, described in greater detail herein. In certainembodiments, a session may be implemented to determine whether or notuser or non-user behaviors enacted during the session are of analyticutility. As an example, certain user or non-user behaviors enactedduring a particular session may indicate the behaviors were enacted byan impostor. As another example, certain user or non-user behaviorsenacted during a particular session may be performed by an authenticatedentity, but the behaviors may be unexpected or out of the norm.

In certain embodiments, two or more sessions may be contiguous. Incertain embodiments, two or more sessions may be noncontiguous, butassociated. In certain embodiments, a session may be associated with twoor more other sessions. In certain embodiments, a session may be asubset of another session. In certain embodiments, the interval of timecorresponding to a first session may overlap an interval of timecorresponding to a second session. In certain embodiments, a session maybe associated with two or more other sessions whose associated intervalsof time may overlap one another. Skilled practitioners of the art willrecognize that many such embodiments are possible. Accordingly, theforegoing is not intended to limit the spirit, scope or intent of theinvention.

The resulting sessions are then ingested in block 916, followed by theperformance of data enrichment operations familiar to those of skill inthe art in block 914. In certain embodiments, user identifierinformation (ID) information provided by a user ID management system 912may be used in block 914 to perform the data enrichment operations. Invarious embodiments, certain contextual information related to aparticular entity behavior or event may be used in block 914 to performthe data enrichment operations. In various embodiments, certain temporalinformation, such as timestamp information, related to a particularentity behavior or event may be used in block 914 to perform the dataenrichment operations. In certain embodiments, a repository of ATP data970 may be implemented to include repositories of entity attribute data920 and behavioral model data 924. In various embodiments, certaininformation stored in the repository of entity attribute data 920 may beused to perform the data enrichment operations in block 914.

In certain embodiments, the resulting enriched sessions may be stored ina repository of raw event data 918. In certain embodiments, theresulting enriched sessions may be provided to a risk services 422module, described in greater detail herein. In certain embodiments, aslikewise described in greater detail herein, the risk services 422module may be implemented to generate inferences, risk models, and riskscores, or a combination thereof. In certain embodiments, the resultinginferences, risk models, or risk scores may then be stored in therepository of behavioral model data.

In certain embodiments, the risk services 422 module may be implementedto provide input data associated with the inferences, risk models, andrisk scores it may generate to a policy service 928. In certainembodiments, the policy service 928 may be implemented to use theinferences, risk models, and risk scores to generate policies. In turn,the policy service 928 may be implemented in certain embodiments toexport the resulting policies to endpoint agents, edge devices, or othersecurity mechanisms, where they may be used to limit risk, as describedin greater detail herein. In certain embodiments, a user interface (UI)or front-end 926 familiar to skilled practitioners of the art may beimplemented to provide administrative access to various components ofthe ATP system 120, as shown in FIG. 9.

FIG. 10 is a table showing components of an adaptive trust profile (ATP)implemented in accordance with an embodiment of the invention. Invarious embodiments, an ATP 640 may be implemented to certain includeentity attributes 1004, behavioral models 1006, and inferences 1008,along with entity state 638. In certain embodiments, an ATP's 640 entitystate 638 may be short-term, or reflect the state of an entity at aparticular point or interval in time. In certain embodiments, an ATP's640 entity state 638 may be long-term, or reflect the state of an entityat recurring points or intervals in time.

In certain embodiments, an ATP's 640 associated entity attributes 1004may be long-lived. As an example, a particular user entity may have aname, an employee ID, an assigned office, and so forth, all of which arefacts rather than insights. In certain embodiments, a particular entitystate 638 may be sufficiently long-termed to be considered an entityattribute 1004. As an example, a first user and a second user may bothhave an entity state 638 of being irritable. However, the first user mayhave a short-term entity state 638 of being irritable on an infrequentbasis, while the second user may have a long-term entity state 638 of beirritable on a recurring basis. In this example, the long-term entitystate 638 of the second user being irritable may be considered to be anentity attribute. In various embodiments, the determination of whatconstitutes an entity state 638 and an entity attribute 1004 is a matterof design choice. In certain embodiments, various knowledgerepresentation approaches may be implemented in combination with an ATPsystem to understand the ontological interrelationship of entityattributes 1004 one or more ATP's 640 may contain. In these embodiments,the method by which certain entity attributes 1004 are selected to betracked by an ATP system, and the method by which they are managedwithin a corresponding ATP 640, is a matter of design choice.

In certain embodiments, the ATP 640 evolves over time as new events andentity behavior is detected. In certain embodiments, an ATP's 640associated behavioral models 1006, and thus the ATP 640 itself mayevolve over time. In certain embodiments, an ATP's 640 behavioral models1006 may be used by an ATP system to provide insight into how unexpecteda set of events may be. As an example, a behavioral model 1006 mayinclude information related to where a particular user entity works,which devices they may use and locations they may login from, who theymay communicate with, and so forth. Certain embodiments of the inventionreflect an appreciation that such behavioral models 1006 can be usefulwhen comparing observe user and non-user behaviors to past observationsin order to determine how unusual a particular action may be.

For example, a user may have more than one ATP 640 associated with aparticular channel, which as used herein broadly refers to a mediumcapable of supporting the electronic observation of a user or non-userbehavior, such as a keyboard, a network, a video stream, and so forth.To continue the example, the user may have a particular set of people hesends emails to from his desktop computer, and does so in an orderly andmethodical manner, carefully choosing his words, and writing longer thanaverage messages compared to his peers. Consequently, analysis of suchan email message will likely indicate it was authored by the user andnot someone else.

However, the same user may also send emails from a second channel, whichis his mobile telephone. When using his mobile telephone, the user'semails are typically short, contains typos and emojis, and his writingstyle is primarily limited to simple confirmations or denials.Consequently, analysis of one such email would likely not reveal whetherthe user was the author or not, due to its brevity. Accordingly, the useof the same channel, which in this example is email, demonstrates theuse of different devices will likely generate different behaviouralmodels 1006, which in turn could affect the veracity of associatedinferences 1008.

In certain embodiments, a behavioral model 1006 may be implemented as asession-based fingerprint. As used herein, a session-based fingerprintbroadly refers to a unique identifier of an enactor of user or non-userbehavior associated with a session. In certain embodiments, thesession-based fingerprint may be implemented to determine how unexpectedan event may be, based upon an entity's history as it relates to therespective history of their peer entities. In certain embodiments, thesession-based fingerprint may be implemented to determine whether anentity associated with a particular session is truly who they or itclaims to be or if they are being impersonated. In certain embodiments,the session-based fingerprint may be implemented to determine whether aparticular event, or a combination thereof, may be of analytic utility.In certain embodiments, the session-based fingerprint may include a riskscore, be used to generate a risk score, or a combination thereof.

As likewise used herein, a fingerprint, as it relates to a session,broadly refers to a collection of information providing one or moredistinctive, characteristic indicators of the identity of an enactor ofone or more corresponding user or non-user entity behaviors during thesession. In certain embodiments, the collection of information mayinclude one or more user or non-user profile elements. A user ornon-user profile element, as used herein, broadly refers to a collectionof user or non-user entity behavior elements, described in greaterdetail herein.

As used herein, inferences 1008 broadly refer to things that can beinferred about an entity based upon observations. In certain embodimentsthe observations may be based upon electronically-observable behavior,described in greater detail herein. In certain embodiments, the behaviormay be enacted by a user entity, a non-user entity, or a combinationthereof. In certain embodiments, inferences 1008 may be used to provideinsight into a user entity's mindset or affective state.

As an example, an inference 1008 may be made that a user is unhappy intheir job or that they are facing significant personal financialpressures. Likewise, based upon the user's observed behavior, aninference 1008 may be made that they are at a higher risk of beingvictimized by phishing schemes due to a propensity for clicking onrandom or risky website links. In certain embodiments, such inferences1008 may be implemented to generate a predictive quantifier of riskassociated with an entity's behavior.

In certain embodiments, entity state 638, described in greater detailherein, may be implemented such that changes in state can beaccommodated quickly while reducing the overall volatility of aparticular ATP 640. As an example, a user may be traveling byautomobile. Accordingly, the user's location is changing quickly.Consequently, location data is short-lived. As a result, while thelocation of the user may not be updated within their associated ATP 640as it changes, the fact their location is changing may prove to beuseful in terms of interpreting other location-based data from othersessions. To continue the example, knowing the user is in the process ofchanging their location may assist in explaining why the user appears tobe in two physical locations at once.

FIG. 11 is a table showing analytic utility actions occurring during asession implemented in accordance with an embodiment of the invention.In certain embodiments, an adaptive trust profile (ATP) system,described in greater detail herein, may be implemented to capture andrecord various actions 1104 enacted by an entity during a session 1102,likewise described in greater detail herein. In certain embodiments, theactions, and their associated sessions, may be stored in an ATPcorresponding to a particular entity. In various embodiments, the ATPsystem may be implemented to process information stored in an ATP todetermine, as described in greater detail herein, which actions 1104enacted by a corresponding entity during a particular session 1102 maybe of analytic utility 1108.

Certain embodiments of the invention reflect an appreciation thatmultiple sessions 1102, each of which may be respectively associatedwith a corresponding entity, may occur within the same interval of time1106. Certain embodiments of the invention likewise reflect anappreciation that a single action of analytic utility 1108 enacted by anentity occurring during a particular interval of time 1106 may notappear to be suspicious behavior by an associated entity. Likewise,certain embodiments of the invention reflect an appreciation that theoccurrence of multiple actions of analytic utility 1108 enacted by anentity during a particular session 1102 may be an indicator ofsuspicious behavior.

Certain embodiments reflect an appreciation that a particular entity maybe associated with two or more sessions 1102 that occur concurrentlyover a period of time 1106. Certain embodiments of the inventionlikewise reflect an appreciation that a single action of analyticutility 1108 enacted by an entity occurring during a first session 1102may not appear to be suspicious. Conversely, certain embodiments of theinvention reflect an appreciation that multiple actions of analyticutility 1108 during a second session 1102 may.

As an example, a user may log into the same system from two different IPaddresses, one associated with their laptop computer and the other theirmobile phone. In this example, actions 1104 enacted by the user usingtheir laptop computer may be associated with a first session 1102 (e.g.session ‘2’), and actions 1104 enacted by the user using their mobilephone may be associated with a second session 1102 (e.g., session ‘3’).To continue the example, only one action of analytic utility 1108 may beassociated with the first session 1102, while three actions of analyticutility 1108 may be associated with the second session 1102.Accordingly, it may be inferred the preponderance of actions of analyticutility 1108 enacted by the user during the second session 1102 mayindicate suspicious behavior being enacted with their mobile phone.

FIG. 12 is a simplified block diagram of an adaptive trust profile (ATP)system environment implemented in accordance with an embodiment of theinvention. In certain embodiments, the ATP system environment may beimplemented to detect user or non-user entity behavior of analyticutility and adaptively respond to mitigate risk. In certain embodiments,the ATP system environment may be implemented to include a securityanalytics system 118. In certain embodiments, the security analyticssystem 118 may be implemented to include an ATP system 120.

In certain embodiments, the ATP system 120, as described in greaterdetail herein, may be implemented to use session-based securityinformation to generate an ATP, likewise describe in greater detailherein. As used herein, session-based security information broadlyrefers to any information associated with a session that can be used todetect entity behavior of analytic utility and mitigate its associatedrisk. In certain embodiments, the session-based security information mayinclude a session-based fingerprint, described in greater detail herein.

In certain embodiments, the security analytics system 118 may beimplemented to use one or more session-based fingerprints to performsecurity analytics operations to detect such user or non-user entitybehavior. In certain embodiments, the security analytics system 118 maybe implemented to monitor user behavior associated with a user entity,such as a user 802. In certain embodiments, the user or non-user entitybehavior is monitored during user/device 830, user/network 842,user/resource 848, and user/user 860 interactions. In certainembodiments, the user/user 860 interactions may occur between a firstuser, such as a first user 802 and a second user 802.

In certain embodiments, as described in greater detail herein, anendpoint agent 306 may be implemented on the endpoint device 304 toperform user or non-user entity behavior monitoring. In certainembodiments, the user or non-user entity behavior may be monitored bythe endpoint agent 306 during user/device 830 interactions between auser entity, such as a user 802, and an endpoint device 304. In certainembodiments, the user or non-user entity behavior may be monitored bythe endpoint agent 306 during user/network 842 interactions between user‘A’ 902 and a network, such as an internal 844 or external 846 network.In certain embodiments, the monitoring of user or non-user entitybehavior by the endpoint agent 306 may include the monitoring ofelectronically-observable actions respectively enacted by a particularuser or non-user entity. In certain embodiments, the endpoint agent 306may be implemented in combination with the security analytics system 118and the ATP system 120 to detect entity behavior of analytic utility andadaptively respond to mitigate risk.

In certain embodiments, the endpoint agent 306 may be implemented toinclude an analytics 310 module and an ATP feature pack 1208. In certainembodiments, the ATP feature pack 1208 may be further implemented toinclude an event data detector 1210 module, an entity behavior detector1212 module, and a session correlation 1214 module. In certainembodiments, the event data detector 1210 module may be implemented todetect event data, described in greater detail herein, resulting fromuser/device 830, user/network 842, user/resource 848, and user/user 860interactions. In various embodiments, the entity behavior detector 1212module may be implemented to detect certain user and non-user entitybehaviors, described in greater detail herein, resulting fromuser/device 830, user/network 842, user/resource 848, and user/user 860interactions.

In various embodiments, the session correlation 1214 module may beimplemented to generate session data by correlating certain eventbehavior data detected by the event data detector 1210 module with aparticular session. In various embodiments, the session correlation 1214module may be implemented to generate session data by correlatingcertain user and non-user entity behavior data detected by the entitybehavior detector 1212 module with a particular session. In certainembodiments, the endpoint agent 306 may be implemented to communicatethe event data detected by the event data detector 1210 module, the userand non-user entity behavior data detected by the entity behaviordetector 1212 module, the session data generated by the session datadetector 1214 module, or a combination thereof, to the securityanalytics 118 system.

In certain embodiments, the security analytics system 118 may beimplemented to receive the event data, the user and non-user entitybehavior data, and the session data provided by the endpoint agent 306.In certain embodiments, the security analytics system 118 may beimplemented to provide the event data, the user and non-user entitybehavior data, and the session data to the ATP system 120 forprocessing. In certain embodiment, the ATP system 120 may be implementedto include an ATP element collector 1282 module, an ATP elementanalytics 1284 module, a session generator 1286 module, a session-basedfingerprint generator 1288 module, an ATP generator 1290 module, or acombination thereof.

In certain embodiments, the ATP element collector 1282 module may beimplemented to process the event data, the user and non-user entitybehavior data, and the session data provided by the endpoint agent 306to generate ATP elements, described in greater detail herein. In variousembodiments, the ATP element analytics 1284 module may be implemented toanalyze certain ATP elements to detect possible user or non-user entitybehavior of analytic utility associated with a particular event. Incertain embodiments, the ATP session generator 1286 module may beimplemented to process the ATP elements collected by the ATP elementcollector 1282 module to generate one or more associated sessions. Incertain embodiments, the session-based fingerprint generator 1288 modulemay be implemented to process the sessions generated by the sessiongenerator 1286 module to generate one or more session-basedfingerprints. In certain embodiments, the ATP generator 1290 module maybe implemented to process the sessions generated by the ATP sessiongenerator 1286 module, the session-based fingerprints generated by thesession-based fingerprint generator 1288 module, or a combinationthereof, to generate am ATP profile, as described in greater detailherein.

FIG. 13 is a generalized flowchart of session-based fingerprintgeneration operations performed in accordance with an embodiment of theinvention. In this embodiment, session-based fingerprint generationoperations are begun in step 1302, followed by the selection of anentity in step 1304 for associated adaptive trust profile (ATP) elementgeneration. As used herein, an ATP element broadly refers to any dataelement stored in an ATP, as described in greater detail herein. Ongoingmonitoring operations are then performed in step 1306 to entity behaviordata associated with the entity selected in step 1304.

A determination is then made in step 1308 whether entity behavior datahas been detected. If not, then a determination is made in step 1326whether to continue monitoring the entity's behavior to detectassociated entity behavior data. If so, then the process is continued,proceeding with step 1306. Otherwise, session-based fingerprintgeneration operations are ended in step 1328. However, if it wasdetermined in step 1308 that entity behavior data was detected, then thedetected entity data is processed in step 1310 to generate an associatedATP element.

A determination is then made in step 1312 whether to generate a new ATPfor the entity. If not, then a target ATP associated with the entity isselected in step 1314. Otherwise, a new ATP for the entity is generatedin step 1316. Thereafter, or once a target ATP associated with theentity has been selected in step 1315, the previously-generated ATPelement is added to the selected or newly-generated ATP in step 1318.

The ATP elements within the ATP are then processed in step 1320 togenerate a session, described in greater detail herein. The resultingsession is in turn processed in step 1322 to generate a correspondingsession-based fingerprint. The session and its correspondingsession-based fingerprint are then associated with the ATP in step 1324that is likewise associated with the entity. The process is thencontinued, proceeding with step 1326.

FIGS. 14a and 14b are a generalized flowchart of the performance ofadaptive trust profile (ATP) definition and management operationsimplemented in accordance with an embodiment of the invention. In thisembodiment, ATP definition and management operations are begun in step1402, followed by ongoing operations being performed by an ATP system instep 1404 to ATP elements, as described in greater detail herein. Adetermination is then made in step 1406 whether an ATP element has beenreceived by the ATP system.

If not, then a determination is made in step 1436 to determine whetherto continue monitoring for ATP elements. If so, then the process iscontinued, proceeding with step 1404. Otherwise, a determination is madein step 1438 whether to end transportable cyberprofile generationoperations. If not, then the process is continued, proceeding with step1404. Otherwise, ATP definition and management operations are ended instep 1438.

However, if it was determined in step 1406 that an ATP element wasreceived, then it is processed in step 1408 to determine its associatedentity. A determination is then made in step 1410 whether to generate anew ATP for the entity. If not, then an ATP associated with the entityis selected in step 1412 and then updated with the ATP element in step1414. However, if it was determined in step 1410 to generate a new ATPfor the entity, then it is generated in step 1416 and populated with theATP element in step 1418.

Thereafter, or after the selected ATP is updated in step 1414, the ATPis processed in step 1420 to perform data enrichment operations,described in greater detail herein. Analytic utility detectionoperations, likewise described in greater detail herein, are thenperformed on the enriched ATP in step 1422 to identify entity behaviorthat may be of analytic utility. Thereafter, a determination is made instep 1424 to determine whether the entity behavior is of analyticutility. If not, then a determination is made in step 1434 whether tocontinue ATP definition and management operations. If so, then theprocess is continued, proceeding with step 1404. Otherwise, ATPdefinition and management operations are ended in step 1438.

However, if it was determined in step 1424 that the entity behavior wasof analytic utility, the contextual information is retrieved in step1426 and then processed in step 1428 with entity attributes stored inthe ATP with a behavioral model to generate one or more inferences,described in greater detail herein. A determination is then made in step1430 whether the one or more inferences indicate behavior by the entityis of analytic utility. If not, the process is continued, proceedingwith step 1434. Otherwise, appropriate risk mitigation operations areperformed in step 1432 and the process is then continued, proceedingwith step 1434.

FIG. 15 is a simplified block diagram of the operation of a securityanalytics system implemented in accordance with an embodiment of theinvention to adaptively assess risk associated with an entity behavior.In this embodiment, entity behavior is monitored and compared to knowngood behavior 1502 and known bad behavior 1504. In typical riskassessment 1506 approaches, low 1508, moderate 1510, or high 1512 riskentity behavior is generally determined by using fairly inflexiblesecurity policies, which are typically used to enact relatively staticresponses.

As an example, a security policy implemented for access control may havea list of actions a particular entity can do and a list of things theycannot. Ordinarily, the actions in those lists are static and don'tchange, regardless of the particular entity behavior being enacted.However, perhaps they should change, or adapt, if it is determined theentity behavior being enacted by the entity has changed, and as aresult, represents a higher risk

To continue the example, a user may be perusing various resources andhappens to access a webpage, such as a shopping site, that containscertain objects. Typical security approaches assume some portion ofthose objects to be good, a small number are known to be bad, and theremainder suspicious. Consequently, there is a continuum of objects,some assumed to be good, some undetermined, and the rest known to bebad. It will be appreciated that the determination of what is consideredto be good, undetermined, or bad is oftentimes fairly arbitrary.

In certain embodiments, contextual information associated with theentity behavior is collected and processed to adaptively respond tochanges in the entity's current behavior. In continuance of the example,the user may change their behavior to access internal businessresources. In this example, accessing internal business resources iscontextual information that may represent the potential for higher risk.As a result, a risk-adaptive behavior system may be implemented incertain embodiments to respond with an adaptive high risk assessment1514. In certain embodiments, the adaptive high risk assessment 1514 isgenerated by a security analytics system, described in greater detailherein. Consequently, the adaptive high risk assessment 1514 mayindicate a larger percentage of certain entity behavior as high 1520risk, and a smaller percentage as low 1516 or moderate 1518 risk.

In further continuance of the example, the user may further change theirbehavior to access an external news site. As before, the user's accessof an external news site is contextual information that may representthe likelihood of lower risk. As a result, the risk-adaptive behaviorsystem may be implemented to respond with an adaptive low riskassessment 1522, which may indicate a larger percentage of certainentity behavior as low 1524 risk, and a smaller percentage as moderate1526 or high 1528 risk.

Certain embodiments of the invention reflect an appreciation thatwithout the described adaptive behavior, the operational overheadadministering entity security would be high, as all entity interactionsrelated to their behavior would continue to be monitored. However, thecost of administering entity security would decrease when the entity wasno longer interacting with internal resources. Consequently, risktolerance can be dynamically adjusted according to the context of agiven entity activity.

More particularly, if the entity's activity is primarily internal to theorganization, then some risk can be tolerated. However, if the entity'sactivity is primarily external to the organization, then it is possiblethat essentially all risk can be tolerated. Furthermore, the userexperience may be more pleasant during non-organization activities, asfewer security controls may be applied or experienced. Moreover, therisk assessment becomes dynamic, according to the identity of theentity, the resources being accessed, their respective behavior, andcorresponding points of observation.

FIG. 16 is a simplified block diagram of the operation of a securityanalytics system implemented in accordance with an embodiment of theinvention to adaptively respond to an entity request. In thisembodiment, an entity may place a request 1606 to download a file from afile source 1608 to a file destination 1604, such as a USB drive. Intraditional security approaches, the owner of the requested file mayhave a single security rule, which would be a granted 1612, or denied1614, request response 1610 as to whether the entity was allowed todownload the file.

In certain embodiments, a risk-adaptive security policy may beimplemented such that the entity's request 1606 to download therequested file is typically granted 1612. However, as an example, a usermay have recently updated their online resume as well as begun to takerandom days off, which may imply a flight risk. By extension, the userbehavior and other actions associated with the user may likewise implythe user's intent to take proprietary information with them to a newjob. Consequently, various risk-adaptive behavior approaches may yield adenied 1614 request response 1610 due to the associated context of theirbehavior, other actions, or a combination thereof.

Alternatively, a risk-adaptive security policy may be implemented invarious embodiments to provide a conditional 1616 request response 1610.As an example, a requested file may be encrypted such that it can onlybe opened on a corporate computer. Furthermore, attempting to open thefile on a non-corporate computer may result in a message being sent to asecurity administrator. Likewise, a single file being downloaded mayappear as good behavior, yet multiple sequential downloads may appearsuspicious, especially if the files do not appear to be related, orpossibly, if they do. From the foregoing, it will be appreciated thatrisk-adaptive behavior is not necessarily based upon an atomic action,but rather a multiplicity of factors, such as contextual informationassociated with particular entity behavior.

FIG. 17 is a generalized flowchart of the performance of securityanalytics system operations implemented in accordance with an embodimentof the invention to adaptively manage entity behavior risk. In thisembodiment, risk-adaptive behavior operations are begun in step 1702,followed by the performance of entity authentication operations,familiar to those of skill in the art, in step 1704. A determination isthen made in step 1706 whether the entity has one or more associatedrisk-adaptive security policies. If so, then they are retrieved for usein step 1708. Thereafter, or if it was determined in step 1706 theentity has no associated risk-adaptive security policies, the entity'sbehavior is monitored in step 1710.

The entity's current user behavior is then processed in step 1712 toidentify any applicable risk-adaptive security policies that may apply.A determination is then made in step 1714 whether any applicablerisk-adaptive security policies have been identified. If not, adetermination is made in step 1726 whether to end risk-adaptive behaviorsystem operations. If not, then the process is continued, proceedingwith step 1710. Otherwise, risk-adaptive behavior system operations areended in step 1728.

However, if it is determined in step 1714 that one or more applicablerisk-adaptive security policies have been identified, then they are usedin step 1716 to process the entity's current user behavior to determinean appropriate risk-adaptive behavior response. The appropriaterisk-adaptive behavior response is then enacted in step 1718, followedby a determination being made in step 1720 whether the risk-adaptivebehavior response enacted in step 1718 indicates acceptable userbehavior.

If so, then the process is continued, proceeding with step 1726.Otherwise the entity's current behavior is determined to be of analyticutility and marked accordingly in step 1722. Entity behavior of analyticutility notification operations are then performed in step 1724. In oneembodiment, entity behavior of analytic utility is stored for laterreview. In another embodiment, a security administrator is notified ofthe enactment of entity behavior of analytic utility. Thereafter, theprocess is continued, proceeding with step 1726.

As will be appreciated by one skilled in the art, the present inventionmay be embodied as a method, system, or computer program product.Accordingly, embodiments of the invention may be implemented entirely inhardware, entirely in software (including firmware, resident software,micro-code, etc.) or in an embodiment combining software and hardware.These various embodiments may all generally be referred to herein as a“circuit,” “module,” or “system.” Furthermore, the present invention maytake the form of a computer program product on a computer-usable storagemedium having computer-usable program code embodied in the medium.

Any suitable computer usable or computer readable medium may beutilized. The computer-usable or computer-readable medium may be, forexample, but not limited to, an electronic, magnetic, optical,electromagnetic, infrared, or semiconductor system, apparatus, ordevice. More specific examples (a non-exhaustive list) of thecomputer-readable medium would include the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a portable compact disc read-only memory (CD-ROM), anoptical storage device, or a magnetic storage device. In the context ofthis document, a computer-usable or computer-readable medium may be anymedium that can contain, store, communicate, or transport the programfor use by or in connection with the instruction execution system,apparatus, or device.

Computer program code for carrying out operations of the presentinvention may be written in an object oriented programming language suchas Java, Smalltalk, C++ or the like. However, the computer program codefor carrying out operations of the present invention may also be writtenin conventional procedural programming languages, such as the “C”programming language or similar programming languages. The program codemay execute entirely on the user's computer, partly on the user'scomputer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer or entirely on the remotecomputer or server. In the latter scenario, the remote computer may beconnected to the user's computer through a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Embodiments of the invention are described with reference to flowchartillustrations and/or block diagrams of methods, apparatus (systems) andcomputer program products according to embodiments of the invention. Itwill be understood that each block of the flowchart illustrations and/orblock diagrams, and combinations of blocks in the flowchartillustrations and/or block diagrams, can be implemented by computerprogram instructions. These computer program instructions may beprovided to a processor of a general purpose computer, special purposecomputer, or other programmable data processing apparatus to produce amachine, such that the instructions, which execute via the processor ofthe computer or other programmable data processing apparatus, createmeans for implementing the functions/acts specified in the flowchartand/or block diagram block or blocks.

These computer program instructions may also be stored in acomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meanswhich implement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus to produce a computer implemented process such that theinstructions which execute on the computer or other programmableapparatus provide steps for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

While particular embodiments of the present invention have been shownand described, it will be obvious to those skilled in the art that,based upon the teachings herein, changes and modifications may be madewithout departing from this invention and its broader aspects.Therefore, the appended claims are to encompass within their scope allsuch changes and modifications as are within the true spirit and scopeof this invention. Furthermore, it is to be understood that theinvention is solely defined by the appended claims. It will beunderstood by those with skill in the art that if a specific number ofan introduced claim element is intended, such intent will be explicitlyrecited in the claim, and in the absence of such recitation no suchlimitation is present. For non-limiting example, as an aid tounderstanding, the following appended claims contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimelements. However, the use of such phrases should not be construed toimply that the introduction of a claim element by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim element to inventions containing only one such element,even when the same claim includes the introductory phrases “one or more”or “at least one” and indefinite articles such as “a” or “an”; the sameholds true for the use in the claims of definite articles.

The present invention is well adapted to attain the advantages mentionedas well as others inherent therein. While the present invention has beendepicted, described, and is defined by reference to particularembodiments of the invention, such references do not imply a limitationon the invention, and no such limitation is to be inferred. Theinvention is capable of considerable modification, alteration, andequivalents in form and function, as will occur to those ordinarilyskilled in the pertinent arts. The depicted and described embodimentsare examples only, and are not exhaustive of the scope of the invention.

Consequently, the invention is intended to be limited only by the spiritand scope of the appended claims, giving full cognizance to equivalentsin all respects.

What is claimed is:
 1. A computer-implementable method for generating anadaptive trust profile, comprising: monitoring a plurality ofelectronically-observable actions of an entity, the plurality ofelectronically-observable actions of the entity corresponding to arespective plurality of events enacted by the entity, the monitoringcomprising monitoring at least one of the plurality ofelectronically-observable actions via a first protected endpoint and atleast another of the plurality of electronically-observable actions viaa second protected endpoint, the first protected endpoint comprising afirst endpoint device and a first endpoint agent; converting theplurality of electronically-observable actions of the entity toelectronic information representing the plurality of actions of theentity; generating the adaptive trust profile based upon the at leastone of the plurality of electronically observable actions of the entityand the at least another of the plurality of electronically-observableactions of the entity, the adaptive trust profile comprising aninference regarding the entity, the inference regarding the entity beingbased upon the plurality of actions of the entity; and, determining, viaa security analytics system, whether an event of the respectiveplurality of events enacted by the entity of analytic utility based uponthe inference regarding the entity.
 2. The method of claim 1, wherein:the at least one of the plurality of electronically-observable actionsis associated with a first session, the first session referring to afirst interval of time; and, the at least another of the plurality ofelectronically-observable actions is associated with a second session,the second session referring to a second interval of time.
 3. The methodof claim 2, wherein: the determining whether an event of the respectiveplurality of events is of analytic utility comprises determining thatthe first session and the second session are associated with acorresponding entity.
 4. The method of claim 1, wherein: the firstprotected endpoint and the second protected endpoint provide adaptivetrust profile information based upon the electronically-observableactions of the entity.
 5. The method of claim 1, wherein: at least oneof the first protected endpoint and the second protected endpointcomprises an analytics module and an adaptive trust profile featurepack.
 6. The method of claim 5, wherein: the adaptive trust profilefeature pack system comprises an event data detector module, an entitybehavior detector module and a session correlation module.
 7. A systemcomprising: a processor; a data bus coupled to the processor; and anon-transitory, computer-readable storage medium embodying computerprogram code for generating an adaptive trust profile, thenon-transitory, computer-readable storage medium being coupled to thedata bus, the computer program code interacting with a plurality ofcomputer operations and comprising instructions executable by theprocessor and configured for: monitoring a plurality ofelectronically-observable actions of an entity, the plurality ofelectronically-observable actions of the entity corresponding to arespective plurality of events enacted by the entity, the monitoringcomprising monitoring at least one of the plurality ofelectronically-observable actions via a first protected endpoint and atleast another of the plurality of electronically-observable actions viaa second protected endpoint, the first protected endpoint comprising afirst endpoint device and a first endpoint agent; converting theplurality of electronically-observable actions of the entity toelectronic information representing the plurality of actions of theentity; generating the adaptive trust profile based upon the at leastone of the plurality of electronically observable actions of the entityand the at least another of the plurality of electronically-observableactions of the entity, the adaptive trust profile comprising aninference regarding the entity, the inference regarding the entity beingbased upon the plurality of actions of the entity; and, determining, viaa security analytics system, whether an event of the respectiveplurality of events enacted by the entity of analytic utility based uponthe inference regarding the entity.
 8. The system of claim 7, wherein:the at least one of the plurality of electronically-observable actionsis associated with a first session, the first session referring to afirst interval of time; and, the at least another of the plurality ofelectronically-observable actions is associated with a second session,the second session referring to a second interval of time.
 9. The systemof claim 8, wherein: the determining whether an event of the respectiveplurality of events is of analytic utility comprises determining thatthe first session and the second session are associated with acorresponding entity.
 10. The system of claim 7, wherein: the firstprotected endpoint and the second protected endpoint provide adaptivetrust profile information based upon the electronically-observableactions of the entity.
 11. The system of claim 7, wherein: at least oneof the first protected endpoint and the second protected endpointcomprises an analytics module and an adaptive trust profile featurepack.
 12. The system of claim 11, wherein: the adaptive trust profilefeature pack system comprises an event data detector module, an entitybehavior detector module and a session correlation module.
 13. Anon-transitory, computer-readable storage medium embodying computerprogram code for generating an adaptive trust profile, the computerprogram code comprising computer executable instructions configured for:monitoring a plurality of electronically-observable actions of anentity, the plurality of electronically-observable actions of the entitycorresponding to a respective plurality of events enacted by the entity,the monitoring comprising monitoring at least one of the plurality ofelectronically-observable actions via a first protected endpoint and atleast another of the plurality of electronically-observable actions viaa second protected endpoint, the first protected endpoint comprising afirst endpoint device and a first endpoint agent; converting theplurality of electronically-observable actions of the entity toelectronic information representing the plurality of actions of theentity; generating the adaptive trust profile based upon the at leastone of the plurality of electronically observable actions of the entityand the at least another of the plurality of electronically-observableactions of the entity, the adaptive trust profile comprising aninference regarding the entity, the inference regarding the entity beingbased upon the plurality of actions of the entity; and, determining, viaa security analytics system, whether an event of the respectiveplurality of events enacted by the entity of analytic utility based uponthe inference regarding the entity.
 14. The non-transitory,computer-readable storage medium of claim 13, wherein: the at least oneof the plurality of electronically-observable actions is associated witha first session, the first session referring to a first interval oftime; and, the at least another of the plurality ofelectronically-observable actions is associated with a second session,the second session referring to a second interval of time.
 15. Thenon-transitory, computer-readable storage medium of claim 14, wherein:the determining whether an event of the respective plurality of eventsis of analytic utility comprises determining that the first session andthe second session are associated with a corresponding entity.
 16. Thenon-transitory, computer-readable storage medium of claim 13, wherein:the first protected endpoint and the second protected endpoint provideadaptive trust profile information based upon theelectronically-observable actions of the entity.
 17. The non-transitory,computer-readable storage medium of claim 13, wherein: at least one ofthe first protected endpoint and the second protected endpoint comprisesan analytics module and an adaptive trust profile feature pack.
 18. Thenon-transitory, computer-readable storage medium of claim 17, wherein:the adaptive trust profile feature pack system comprises an event datadetector module, an entity behavior detector module and a sessioncorrelation module.
 19. The non-transitory, computer-readable storagemedium of claim 13, wherein: the computer executable instructions aredeployable to a client system from a server system at a remote location.20. The non-transitory, computer-readable storage medium of claim 13,wherein: the computer executable instructions are provided by a serviceprovider to a user on an on-demand basis.